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Holt NM, Byrne MF. The Role of Artificial Intelligence and Big Data for Gastrointestinal Disease. Gastrointest Endosc Clin N Am 2025; 35:291-308. [PMID: 40021230 DOI: 10.1016/j.giec.2024.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2025]
Abstract
Artificial intelligence (AI) is a rapidly evolving presence in all fields and industries, with the ability to both improve quality and reduce the burden of human effort. Gastroenterology is a field with a focus on diagnostic techniques and procedures, and AI and big data have established and growing roles to play. Alongside these opportunities are challenges, which will evolve in parallel.
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Affiliation(s)
- Nicholas Mathew Holt
- Gastroenterology and Hepatology Unit, The Canberra Hospital, Yamba Drive, Garran, ACT 2605, Australia.
| | - Michael Francis Byrne
- Division of Gastroenterology, Vancouver General Hospital, University of British Columbia, UBC Division of Gastroenterology, 5153 - 2775 Laurel Street, Vancouver, British Columbia V5Z 1M9, Canada
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Argumánez V, Plumé G, Bustamante-Balén M. Periendoscopic management of antithrombotic drugs: Is cold snare polypectomy the answer? GASTROENTEROLOGIA Y HEPATOLOGIA 2025; 48:502286. [PMID: 39549819 DOI: 10.1016/j.gastrohep.2024.502286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2024] [Revised: 10/27/2024] [Accepted: 10/29/2024] [Indexed: 11/18/2024]
Abstract
Patients undergoing colonoscopy are increasingly taking antithrombotic medication. These patients, who are generally older, also have a higher prevalence of colon polyps. Therefore, it is general practice to modify or discontinue antithrombotic treatment before colonoscopy, to reduce the risk of post-polypectomy bleeding (PPB). However, this modification increases the risk of thrombotic events. Currently, the main clinical guidelines recommend cold-snare resection for polyps smaller than 10mm because of its better safety profile, especially in reducing post-polypectomy bleeding. This reduced PPB rate could open the door to maintaining antithrombotic drugs in patients undergoing colonoscopy. This review aims to evaluate the evidence available so far that may support a modification of current guidelines on the management of antithrombotic drugs in the periendoscopic period.
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Affiliation(s)
- Victor Argumánez
- Gastrointestinal Endoscopy Unit, Digestive Diseases Department, Hospital Universitari i Politècnic La Fe, Valencia 46026, Spain
| | - Gema Plumé
- Hematology Department, Hospital Universitari i Politècnic La Fe, Valencia 46026, Spain
| | - Marco Bustamante-Balén
- Gastrointestinal Endoscopy Unit, Digestive Diseases Department, Hospital Universitari i Politècnic La Fe, La Fe Health Research Institute (IISLaFe), Valencia 46026, Spain.
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McEntee PD, Singaravelu A, McCarrick CA, Murphy E, Boland PA, Cahill RA. Quantification of indocyanine green fluorescence angiography in colorectal surgery: a systematic review of the literature. Surg Endosc 2025; 39:2677-2691. [PMID: 40019566 PMCID: PMC11933147 DOI: 10.1007/s00464-025-11621-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2024] [Accepted: 02/15/2025] [Indexed: 03/01/2025]
Abstract
BACKGROUND Indocyanine green fluorescence angiography (ICGFA) during colorectal surgery associates with reduced post-operative anastomotic complication rates. Because its interpretation is subjective, quantification has been proposed to address inter-user variability. This study reviews the published literature regarding ICGFA quantification during colorectal surgery with a focus on impactful clinical deployment. METHODS A systematic review was performed of English language publications regarding clinical studies of ICGFA quantification in colorectal surgery in PubMed, Scopus, Web of Science and Cochrane Library on 29th August 2024, updated to 18th November 2024, following PRISMA guidelines. Newcastle Ottawa scale (NOS) was used to assess quality. RESULTS A total of 1428 studies were screened with 22 studies (1469 patients) selected. There was significant heterogeneity of ICGFA methodology, quantification methods and parameter selection and only three studies were NOS "high" quality. Extracorporeal application was most common. Four studies (154 patients) conducted real-time ICGFA analyses (others were post hoc) and four utilised artificial intelligence methods. Eleven studies only included patients undergoing left-sided resection (six focusing specifically on rectal resections). Only one study employed the quantification method to guide intra-operative decision-making regarding colonic transection. Twenty-six different perfusion parameters were assessed, with time from injection to visible fluorescence and maximum intensity the most commonly (but not only) correlated parameters regarding anastomotic complication (n = 18). Other grounding correlates were tissue oxygenation (n = 3, two with hyperspectral imagery), metabolites (n = 2) and surgeon interpretation (n = 5). CONCLUSION Quantification of the ICGFA signal for colorectal surgery is feasible but has so far seen limited academic advancement beyond feasibility.
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Affiliation(s)
- Philip D McEntee
- UCD Centre for Precision Surgery, UCD, 47 Eccles Street, Dublin 7, Dublin, Ireland
- Department of Surgery, Mater Misericordiae University Hospital, Dublin, Ireland
| | | | - Cathleen A McCarrick
- UCD Centre for Precision Surgery, UCD, 47 Eccles Street, Dublin 7, Dublin, Ireland
- Department of Surgery, Mater Misericordiae University Hospital, Dublin, Ireland
| | - Edward Murphy
- UCD Centre for Precision Surgery, UCD, 47 Eccles Street, Dublin 7, Dublin, Ireland
- Department of Surgery, Mater Misericordiae University Hospital, Dublin, Ireland
| | - Patrick A Boland
- UCD Centre for Precision Surgery, UCD, 47 Eccles Street, Dublin 7, Dublin, Ireland
- Department of Surgery, Mater Misericordiae University Hospital, Dublin, Ireland
| | - Ronan A Cahill
- UCD Centre for Precision Surgery, UCD, 47 Eccles Street, Dublin 7, Dublin, Ireland.
- Department of Surgery, Mater Misericordiae University Hospital, Dublin, Ireland.
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Zhou S, Xie Y, Feng X, Li Y, Shen L, Chen Y. Artificial intelligence in gastrointestinal cancer research: Image learning advances and applications. Cancer Lett 2025; 614:217555. [PMID: 39952597 DOI: 10.1016/j.canlet.2025.217555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2024] [Revised: 01/31/2025] [Accepted: 02/11/2025] [Indexed: 02/17/2025]
Abstract
With the rapid advancement of artificial intelligence (AI) technologies, including deep learning, large language models, and neural networks, these methodologies are increasingly being developed and integrated into cancer research. Gastrointestinal tumors are characterized by complexity and heterogeneity, posing significant challenges for early detection, diagnostic accuracy, and the development of personalized treatment strategies. The application of AI in digestive oncology has demonstrated its transformative potential. AI not only alleviates the diagnostic burden on clinicians, but it improves tumor screening sensitivity, specificity, and accuracy. Additionally, AI aids the detection of biomarkers such as microsatellite instability and mismatch repair, supports intraoperative assessments of tumor invasion depth, predicts treatment responses, and facilitates the design of personalized treatment plans to potentially significantly enhance patient outcomes. Moreover, the integration of AI with multiomics analyses and imaging technologies has led to substantial advancements in foundational research on the tumor microenvironment. This review highlights the progress of AI in gastrointestinal oncology over the past 5 years with focus on early tumor screening, diagnosis, molecular marker identification, treatment planning, and prognosis predictions. We also explored the potential of AI to enhance medical imaging analyses to aid tumor detection and characterization as well as its role in automating and refining histopathological assessments.
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Affiliation(s)
- Shengyuan Zhou
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yi Xie
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Xujiao Feng
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yanyan Li
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Lin Shen
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yang Chen
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China; Department of Gastrointestinal Cancer, Beijing GoBroad Hospital, Beijing, 102200, China.
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Bustamante-Balén M, Merino Rodríguez B, Barranco L, Monje J, Álvarez M, de Pedro S, Oyagüez I, Van Lent N, Mareque M. Cost-effectiveness analysis of artificial intelligence-aided colonoscopy for adenoma detection and characterization in Spain. Endosc Int Open 2025; 13:a25097278. [PMID: 40109314 PMCID: PMC11922311 DOI: 10.1055/a-2509-7278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Accepted: 12/17/2024] [Indexed: 03/22/2025] Open
Abstract
Background and study aims The aim of this study was to assess the cost-effectiveness of an intelligent endoscopy module for computer-assisted detection and characterization (CADe/CADx) compared with standard practice, from a Spanish National Health System perspective. Methods A Markov model was designed to estimate total costs, life years gained (LYG), and quality-adjusted life years (QALYs) over a lifetime horizon with annual cycles. A hypothetical cohort of 1,000 patients eligible for colonoscopy (mean age 61.32 years) was distributed between Markov states according to polyp size, location, and histology based on national screening program data. CADe/CADx efficacy was determined based on adenoma miss rates and natural disease evolution was simulated according to annual transition probabilities. Detected polyp management involved polypectomy and histopathology in standard practice, whereas with CADe/CADx leave-in-situ strategy was applied for ≤ 5 mm rectosigmoid non-adenomas and resect-and-discard strategy for the rest of ≤ 5mm polyps. Unit costs (€,2024) included the diagnostic procedure and polyp and colorectal cancer (CRC) management. A 3% annual discount rate was applied to costs and outcomes. Model inputs were validated by an expert panel. Results CADe/CADx was more effective (16.37 LYG and 14.32 QALYs) than standard practice (16.33 LYG and 14.27 QALYs) over a lifetime horizon. Total cost per patient was €2,300.76 with CADe/CADx and €2,508.75 with colonoscopy alone. In a hypothetical cohort of 1,000 patients, CADe/CADx avoided 173 polypectomies, 370 histopathologies, and 7 CRC cases. Sensitivity analyses confirmed model robustness. Conclusions The results of this analysis suggest that CADe/CADx would result in a dominant strategy versus standard practice in patients undergoing colonoscopy in Spain.
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Affiliation(s)
- Marco Bustamante-Balén
- Digestive Endoscopy Unit, Gastroenterology Department, La Fe University Hospital, Valencia, Spain
- Health Research Institute La Fe, IIS La Fe, Valencia, Spain
| | - Beatriz Merino Rodríguez
- Endoscopy Unit, Department of Gastroenterology, Gregorio Marañón General University Hospital, Madrid, Spain
| | - Luis Barranco
- Endoscopy Unit, Digestive Department, Hospital del Mar, Barcelona, Spain
| | - Julen Monje
- Health Economics & Outcomes Research Unit, Medtronic Ibérica S.A., Madrid, Spain
| | - María Álvarez
- Health Economics & Outcomes Research Unit, Medtronic Ibérica S.A., Madrid, Spain
| | - Sofía de Pedro
- Pharmacoeconomics & Outcomes Research Iberia (PORIB), Madrid, Spain
| | - Itziar Oyagüez
- Pharmacoeconomics & Outcomes Research Iberia (PORIB), Madrid, Spain
| | - Nancy Van Lent
- Health Economics & Outcomes Research, Medtronic International Trading Sarl, Tolochenaz, Switzerland
| | - María Mareque
- Pharmacoeconomics & Outcomes Research Iberia (PORIB), Madrid, Spain
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Hassan C, Bisschops R, Sharma P, Mori Y. Colon Cancer Screening, Surveillance, and Treatment: Novel Artificial Intelligence Driving Strategies in the Management of Colon Lesions. Gastroenterology 2025:S0016-5085(25)00478-0. [PMID: 40054749 DOI: 10.1053/j.gastro.2025.02.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2024] [Revised: 02/09/2025] [Accepted: 02/15/2025] [Indexed: 03/25/2025]
Abstract
Colonoscopy, a crucial procedure for detecting and removing colorectal polyps, has seen transformative advancements through the integration of artificial intelligence, specifically in computer-aided detection (CADe) and diagnosis (CADx). These tools enhance real-time detection and characterization of lesions, potentially reducing human error, and standardizing the quality of colonoscopy across endoscopists. CADe has proven effective in increasing adenoma detection rate, potentially reducing long-term colorectal cancer incidence. However, CADe's benefits are accompanied by challenges, such as potentially longer procedure times, increased non-neoplastic polyp resections, and a higher surveillance burden. CADx, although promising in differentiating neoplastic and non-neoplastic diminutive polyps, encounters limitations in accuracy, particularly in the proximal colon. Real-world data also revealed gaps between trial efficacy and practical outcomes, emphasizing the need for further research in uncontrolled settings. Moreover, CADx limited specificity and binary output underscore the necessity for explainable artificial intelligence to gain endoscopists' trust. This review aimed to explore the benefits, harms, and limitations of artificial intelligence for colon cancer screening, surveillance, and treatment focusing on CADe and CADx systems for lesion detection and characterization, respectively, while addressing challenges in integrating these technologies into clinical practice.
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Affiliation(s)
- Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Department of Gastroenterology, Istituto di Ricovero e Cura a Carattere Scientifico, Humanitas Research Hospital, Rozzano, Italy.
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium; Translational Research Center in Gastrointestinal Disorders, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, Kansas City Veterans Affairs Medical Center, Kansas City, Missouri
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan; Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
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Kumar A, Aravind N, Gillani T, Kumar D. Artificial intelligence breakthrough in diagnosis, treatment, and prevention of colorectal cancer – A comprehensive review. Biomed Signal Process Control 2025; 101:107205. [DOI: 10.1016/j.bspc.2024.107205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2024]
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Rizkala T, Hassan C, Mori Y, Spadaccini M, Antonelli G, Dekker E, Houwen BBSL, Pech O, Baumer S, Rondonotti E, Radaelli F, Li JW, von Renteln D, Misawa M, Facciorusso A, Maselli R, Carrara S, Fugazza A, Capogreco A, Khalaf K, Patel H, Sharma P, Rex D, Repici A. Accuracy of Computer-aided Diagnosis in Colonoscopy Varies According to Polyp Location: A Systematic Review and Meta-analysis. Clin Gastroenterol Hepatol 2025; 23:531-541. [PMID: 39209199 DOI: 10.1016/j.cgh.2024.08.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 08/02/2024] [Accepted: 08/08/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND & AIMS Computer-aided diagnosis (CADx) assists endoscopists in differentiating between neoplastic and non-neoplastic polyps during colonoscopy. This study aimed to evaluate the impact of polyp location (proximal vs. distal colon) on the diagnostic performance of CADx for ≤5 mm polyps. METHODS We searched for studies evaluating the performance of real-time CADx alone (ie, independently of endoscopist judgement) for predicting the histology of colorectal polyps ≤5 mm. The primary endpoints were CADx sensitivity and specificity in the proximal and distal colon. Secondary outcomes were the negative predictive value (NPV), positive predictive value (PPV), and the accuracy of the CADx alone. Distal colon was limited to the rectum and sigmoid. RESULTS We included 11 studies for analysis with a total of 7782 polyps ≤5 mm. CADx specificity was significantly lower in the proximal colon compared with the distal colon (62% vs 85%; risk ratio (RR), 0.74; 95% confidence interval [CI], 0.72-0.84). Conversely, sensitivity was similar (89% vs 87%); RR, 1.00; 95% CI, 0.97-1.03). The NPV (64% vs 93%; RR, 0.71; 95% CI, 0.64-0.79) and accuracy (81% vs 86%; RR, 0.95; 95% CI, 0.91-0.99) were significantly lower in the proximal than distal colon, whereas PPV was higher in the proximal colon (87% vs 76%; RR, 1.11; 95% CI, 1.06-1.17). CONCLUSION The diagnostic performance of CADx for polyps in the proximal colon is inadequate, exhibiting significantly lower specificity compared with its performance for distal polyps. Although current CADx systems are suitable for use in the distal colon, they should not be employed for proximal polyps until more performant systems are developed specifically for these lesions.
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Affiliation(s)
- Tommy Rizkala
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Cesare Hassan
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan; University of Oslo, Clinical Effectiveness Research Group, Oslo, Norway; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
| | | | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli, Ariccia, Rome, Italy; Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, "Sapienza" University of Rome, Italy
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, the Netherlands; Bergman Clinics Maag and Darm Amsterdam, Amsterdam, The Netherlands
| | - Britt B S L Houwen
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Oliver Pech
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Sebastian Baumer
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | | | | | - James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore
| | - Daniel von Renteln
- Montreal University Hospital Research Center, Montreal, Quebec, Canada; Division of Gastroenterology, Montreal University Hospital Center (CHUM), Montreal, Quebec, Canada
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Antonio Facciorusso
- University of Foggia, Department of Medical Sciences, Section of Gastroenterology, Foggia, Italy
| | - Roberta Maselli
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Silvia Carrara
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | | | | | - Kareem Khalaf
- Division of Gastroenterology, St Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Harsh Patel
- Department of Gastroenterology and Hepatology, Kansas City VA Medical Center, Kansas City, Missouri
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, Kansas City VA Medical Center, Kansas City, Missouri
| | - Douglas Rex
- Division of Gastroenterology, Indiana University School of Medicine, Indianapolis, Indiana
| | - Alessandro Repici
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
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Ang TL, Hang DV, Li JW, Ho JCL, Sy-Janairo ML, Raja Ali RA, Makharia GK, Sundaram S, Chantarojanasiri T, Kim HG, Isayama H, Pausawasdi N, Wu K, Syam AF, Aye TT, Rehman S, Niriella MA, Jurawan R, Wang L, Leung WK, Liou JM, Rizan C, Wu JCY, Ooi CJ. APAGE Position Statements on Green and Sustainability in Gastroenterology, Hepatology, and Gastrointestinal Endoscopy. J Gastroenterol Hepatol 2025. [PMID: 39888113 DOI: 10.1111/jgh.16896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2024] [Revised: 01/13/2025] [Accepted: 01/18/2025] [Indexed: 02/01/2025]
Abstract
BACKGROUND AND AIM The APAGE Position Statements aimed to provide guidance to healthcare practitioners on clinical practices aligned with climate sustainability. METHODS A taskforce convened by APAGE proposed provisional statements. Twenty-two gastroenterologists from the Asian Pacific region participated in online voting and consensus was assessed through an anonymized and iterative Delphi process. RESULTS There were five sections that addressed the rationale for climate action, the importance of adopting principles of waste management, clinical practice, gastrointestinal endoscopy, and issues related to advocacy and research. Sixteen statements achieved consensus and included the following: 1. APAGE recommends adopting prompt measures to reduce the carbon footprint of clinical practice due to the importance of climate action and its health cobenefits. 5. APAGE recommends adherence to professional clinical guidelines to optimize clinical care delivery in gastroenterology and hepatology to avoid the environmental impact of unnecessary procedures and tests. 8. APAGE recommends an emphasis on health promotion, disease prevention, and appropriate screening and surveillance, when resources are available, to reduce the environmental impact of managing more advanced diseases that require more intensive resources. 12. APAGE recommends that technological advances in endoscopic imaging and artificial intelligence, when available, be used to improve the precision of endoscopic diagnosis to reduce the risk of missed lesions and need for unnecessary biopsies. 13. APAGE recommends against the routine use of single-use endoscopes. CONCLUSION The position statements provide guidance to healthcare practitioners on clinical practices in gastroenterology, hepatology, and endoscopy that promote climate sustainability.
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Affiliation(s)
- Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Duke-NUS Medical School, Yong Loo Lin School of Medicine, National University of Singapore, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Dao Viet Hang
- Endoscopy Centre, Hanoi Medical University Hospital, Hanoi, Vietnam
| | - James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Duke-NUS Medical School, Yong Loo Lin School of Medicine, National University of Singapore, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Jacky Chiu Leung Ho
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | | | | | - Govind K Makharia
- Department of Gastroenterology and Human Nutrition, All India Institute of Medical Sciences, New Delhi, India
| | - Sridhar Sundaram
- Department of Digestive Diseases and Clinical Nutrition, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India
| | - Tanyaporn Chantarojanasiri
- Division of Gastroenterology, Department of Internal Medicine, Rajavithi Hospital, Rangsit University, Bangkok, Thailand
| | - Hyun-Gun Kim
- Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, Korea
| | - Hiroyuki Isayama
- Department of Gastroenterology, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Nonthalee Pausawasdi
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Kaichun Wu
- Fourth Military Medical University, Xijing Hospital, Xian, China
| | - Ari Fahrial Syam
- Department of Internal Medicine, Faculty of Medicine, University of Indonesia, Jakarta, Indonesia
| | - Than Than Aye
- Department of Gastroenterology, Yangon General Hospital. University of Medicine 1, Yangon, Myanmar
| | - Sher Rehman
- Department of Gastroenterology, Khyber Girls Medical College, Hayat Abad Medical Complex, Peshawar, Pakistan
| | - Madunil Anuk Niriella
- Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
| | - Ricardo Jurawan
- Taranaki Base Hospital, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Liangjing Wang
- Second Affiliated Hospital of Zhejiang, University School of Medicine, Hangzhou, China
| | - Wai Keung Leung
- Department of Medicine, Queen Mary Hospital, The University of Hong Kong, Hong Kong SAR, China
| | - Jyh-Ming Liou
- College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Chantelle Rizan
- Centre for Sustainable Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Justin Che Yuen Wu
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Choon Jin Ooi
- Duke-NUS Medical School, Gleneagles Medical Centre, Singapore
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Misawa M, Kudo SE. Current Status of Artificial Intelligence Use in Colonoscopy. Digestion 2024; 106:138-145. [PMID: 39724867 DOI: 10.1159/000543345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2024] [Accepted: 12/24/2024] [Indexed: 12/28/2024]
Abstract
BACKGROUND Artificial intelligence (AI) has significantly impacted medical imaging, particularly in gastrointestinal endoscopy. Computer-aided detection and diagnosis systems (CADe and CADx) are thought to enhance the quality of colonoscopy procedures. SUMMARY Colonoscopy is essential for colorectal cancer screening but often misses a significant percentage of adenomas. AI-assisted systems employing deep learning offer improved detection and differentiation of colorectal polyps, potentially increasing adenoma detection rates by 8%-10%. The main benefit of CADe is in detecting small adenomas, whereas it has a limited impact on advanced neoplasm detection. Recent advancements include real-time CADe systems and CADx for histopathological predictions, aiding in the differentiation of neoplastic and nonneoplastic lesions. Biases such as the Hawthorne effect and potential overdiagnosis necessitate large-scale clinical trials to validate the long-term benefits of AI. Additionally, novel concepts such as computer-aided quality improvement systems are emerging to address limitations facing current CADe systems. KEY MESSAGES Despite the potential of AI for enhancing colonoscopy outcomes, its effectiveness in reducing colorectal cancer incidence and mortality remains unproven. Further prospective studies are essential to establish the overall utility and clinical benefits of AI in colonoscopy.
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Affiliation(s)
- Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Tsuzuki, Yokohama, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Tsuzuki, Yokohama, Japan
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Djinbachian R, El Yamani MEM, Rex DK, Pohl H, Taghiakbari M, von Renteln D. Using Computer-aided Optical Diagnosis and Expert Review to Evaluate Colorectal Polyps Diagnosed as Normal Mucosa in Pathology. Clin Gastroenterol Hepatol 2024; 22:2344-2346.e1. [PMID: 38705436 DOI: 10.1016/j.cgh.2024.03.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 03/20/2024] [Accepted: 03/26/2024] [Indexed: 05/07/2024]
Abstract
Pathological assessment of colorectal polyps is considered the current reference standard for histologic diagnosis. About 10% of polyps sent to the pathology lab are returned with the diagnosis of mucosal folds, mucosal prolapse, or normal mucosa.1,2 Two recent publications have indicated that disagreements between endoscopic optical diagnosis and the subsequent pathological diagnoses might be due to misdiagnosis in pathology.3,4 We were therefore interested in re-evaluating pathology-based diagnosis of "mucosal polyps" using expert endoscopists and computer-assisted diagnosis (CADx) evaluation.
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Affiliation(s)
- Roupen Djinbachian
- Division of Gastroenterology, University of Montreal Hospital Center (CHUM), Montreal, Quebec, Canada; University of Montreal Hospital Research Center (CRCHUM), Montreal, Quebec, Canada
| | | | - Douglas K Rex
- Division of Gastroenterology/Hepatology, Indiana University School of Medicine, Indianapolis, Indiana
| | - Heiko Pohl
- Department of Veterans Affairs Medical Center, White River Junction, Vermont; The Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| | - Mahsa Taghiakbari
- University of Montreal Hospital Research Center (CRCHUM), Montreal, Quebec, Canada
| | - Daniel von Renteln
- Division of Gastroenterology, University of Montreal Hospital Center (CHUM), Montreal, Quebec, Canada; University of Montreal Hospital Research Center (CRCHUM), Montreal, Quebec, Canada.
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12
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Hassan C, Rizkala T, Mori Y, Spadaccini M, Misawa M, Antonelli G, Rondonotti E, Dekker E, Houwen BBSL, Pech O, Baumer S, Li JW, von Renteln D, Haumesser C, Maselli R, Facciorusso A, Correale L, Menini M, Schilirò A, Khalaf K, Patel H, Radadiya DK, Bhandari P, Kudo SE, Sultan S, Vandvik PO, Sharma P, Rex DK, Foroutan F, Repici A. Computer-aided diagnosis for the resect-and-discard strategy for colorectal polyps: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol 2024; 9:1010-1019. [PMID: 39303733 DOI: 10.1016/s2468-1253(24)00222-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 06/21/2024] [Accepted: 07/02/2024] [Indexed: 09/22/2024]
Abstract
BACKGROUND The resect-and-discard strategy allows endoscopists to replace post-polypectomy pathology with real-time prediction of polyp histology during colonoscopy (optical diagnosis). We aimed to investigate the benefits and harms of implementing computer-aided diagnosis (CADx) for polyp pathology into the resect-and-discard strategy. METHODS In this systematic review and meta-analysis, we searched MEDLINE, Embase, and Scopus from database inception to June 5, 2024, without language restrictions, for diagnostic accuracy studies that assessed the performance of real-time CADx systems, compared with histology, for the optical diagnosis of diminutive polyps (≤5 mm) in the entire colon. We synthesised data for three strategies: CADx-alone, CADx-unassisted, and CADx-assisted; when the endoscopist was involved in the optical diagnosis, we synthesised data exclusively from diagnoses for which confidence in the prediction was reported as high. The primary outcomes were the proportion of polyps that would have avoided pathological assessment (ie, the proportion optically diagnosed with high confidence; main benefit) and the proportion of polyps incorrectly predicted due to false positives and false negatives (main harm), directly compared between CADx-assisted and CADx-unassisted strategies. We used DerSimonian and Laird's random-effects model to calculate all outcomes. We used Higgins I2 to assess heterogeneity, the Grading of Recommendations, Assessment, Development, and Evaluation approach to rate certainty, and funnel plots and Egger's test to examine publication bias. This study is registered with PROSPERO, CRD42024508440. FINDINGS We found 1019 studies, of which 11 (7400 diminutive polyps, 3769 patients, and 185 endoscopists) were included in the final meta-analysis. Three studies (1817 patients and 4086 polyps [2148 neoplastic and 1938 non-neoplastic]) provided data to directly compare the primary outcome measures between the CADx-unassisted and CADx-assisted strategies. We found no significant difference between the CADx-assisted and CADx-unassisted strategies for the proportion of polyps that would have avoided pathological assessment (90% [88-93], 3653 [89·4%] of 4086 polyps diagnosed with high confidence vs 90% [95% CI 85-94], 3588 [87·8%] of 4086 polyps diagnosed with high confidence; risk ratio 1·01 [95% CI 0·99-1·04; I2=53·49%; low-certainty evidence; Egger's test p=0·18). The proportion of incorrectly predicted polyps was lower with the CADx-assisted strategy than with the CADx-unassisted strategy (12% [95% CI 7-17], 523 [14·3%] of 3653 polyps incorrectly predicted with a CADx-assisted strategy vs 13% [6-20], 582 [16·2%] of 3588 polyps incorrectly diagnosed with a CADx-unassisted strategy; risk ratio 0·88 [95% CI 0·79-0·98]; I2=0·00%; low-certainty evidence; Egger's test p=0·18). INTERPRETATION CADx did not produce benefit nor harm for the resect-and-discard strategy, questioning its value in clinical practice. Improving the accuracy and explainability of CADx is desired. FUNDING European Commission (Horizon Europe), the Japan Society of Promotion of Science, and Associazione Italiana per la Ricerca sul Cancro.
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Affiliation(s)
- Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, Humanitas Clinical and Research Center, IRCCS, Rozzano, Italy
| | - Tommy Rizkala
- Endoscopy Unit, Humanitas Clinical and Research Center, IRCCS, Rozzano, Italy
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan; Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway.
| | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, Humanitas Clinical and Research Center, IRCCS, Rozzano, Italy
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli, Rome, Italy; Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, Sapienza University of Rome, Rome, Italy
| | | | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, Netherlands; Bergman Clinics Maag and Darm Amsterdam, Amsterdam, Netherlands
| | - Britt B S L Houwen
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, Netherlands
| | - Oliver Pech
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Sebastian Baumer
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore
| | - Daniel von Renteln
- Montreal University Hospital Research Center, Montreal, QC, Canada; Division of Gastroenterology, Montreal University Hospital Center, Montreal, QC, Canada
| | - Claire Haumesser
- Montreal University Hospital Research Center, Montreal, QC, Canada
| | - Roberta Maselli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, Humanitas Clinical and Research Center, IRCCS, Rozzano, Italy
| | - Antonio Facciorusso
- Department of Medical Sciences, Section of Gastroenterology, University of Foggia, Foggia, Italy
| | - Loredana Correale
- Endoscopy Unit, Humanitas Clinical and Research Center, IRCCS, Rozzano, Italy
| | - Maddalena Menini
- Endoscopy Unit, Humanitas Clinical and Research Center, IRCCS, Rozzano, Italy
| | - Alessandro Schilirò
- Endoscopy Unit, Humanitas Clinical and Research Center, IRCCS, Rozzano, Italy
| | - Kareem Khalaf
- Division of Gastroenterology St Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | - Harsh Patel
- Kansas City VA Medical Center, Gastroenterology and Hepatology, Kansas City, MO, USA
| | - Dhruvil K Radadiya
- Kansas City VA Medical Center, Gastroenterology and Hepatology, Kansas City, MO, USA
| | - Pradeep Bhandari
- Department of Gastroenterology, Queen Alexandra Hospital, Portsmouth, UK
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Shahnaz Sultan
- Division of Gastroenterology, Hepatology, and Nutrition, University of Minnesota, Minneapolis, MN, USA; Veterans Affairs Healthcare System, Minneapolis, MN, USA
| | - Per Olav Vandvik
- Department of Medicine, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Prateek Sharma
- Kansas City VA Medical Center, Gastroenterology and Hepatology, Kansas City, MO, USA
| | - Douglas K Rex
- Division of Gastroenterology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Farid Foroutan
- Ted Rogers Centre for Heart Research, University Health Network, Toronto, ON, Canada
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, Humanitas Clinical and Research Center, IRCCS, Rozzano, Italy
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13
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Sato K, Kuramochi M, Tsuchiya A, Yamaguchi A, Hosoda Y, Yamaguchi N, Nakamura N, Itoi Y, Hashimoto Y, Kasuga K, Tanaka H, Kuribayashi S, Takeuchi Y, Uraoka T. Multicentre study to assess the performance of an artificial intelligence instrument to support qualitative diagnosis of colorectal polyps. BMJ Open Gastroenterol 2024; 11:e001553. [PMID: 39438054 PMCID: PMC11499753 DOI: 10.1136/bmjgast-2024-001553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2024] [Accepted: 09/30/2024] [Indexed: 10/25/2024] Open
Abstract
OBJECTIVE Computer-aided diagnosis (CAD) using artificial intelligence (AI) is expected to support the characterisation of colorectal lesions, which is clinically relevant for efficient colorectal cancer prevention. We conducted this study to assess the diagnostic performance of commercially available CAD systems. METHODS This was a multicentre, prospective performance evaluation study. The endoscopist diagnosed polyps using white light imaging, followed by non-magnified blue light imaging (non-mBLI) and mBLI. AI subsequently assessed the lesions using non-mBLI (non-mAI), followed by mBLI (mAI). Eventually, endoscopists made the final diagnosis by integrating the AI diagnosis (AI+endoscopist). The primary endpoint was the accuracy of the AI diagnosis of neoplastic lesions. The diagnostic performance of each modality (sensitivity, specificity and accuracy) and confidence levels were also assessed. RESULTS Overall, 380 lesions from 139 patients were included in the analysis. The accuracy of non-mAI was 83%, 95% CI (79% to 87%), which was inferior to that of mBLI (89%, 95% CI (85% to 92%)) and mAI (89%, 95% CI (85% to 92%)). The accuracy (95% CI) of diagnosis by expert endoscopists using mAI (91%, 95% CI (87% to 94%)) was comparable to that of expert endoscopists using mBLI (91%, 95% CI (87% to 94%)) but better than that of non-expert endoscopists using mAI (83%, 95% CI (75% to 90%)). The level of confidence in making a correct diagnosis was increased when using magnification and AI. CONCLUSIONS The diagnostic performance of mAI for differentiating colonic lesions is comparable to that of endoscopists, regardless of their experience. However, it can be affected by the use of magnification as well as the endoscopists' level of experience.
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Affiliation(s)
- Keigo Sato
- Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine School of Medicine Faculty of Medicine, Maebashi, Gunma, Japan
| | - Mizuki Kuramochi
- Department of Gastroenterology, National Hospital Organization Saitama Hospital, Wako, Saitama, Japan
| | | | - Akihiro Yamaguchi
- Department of Gastroenterology, National Hospital Organization Saitama Hospital, Wako, Saitama, Japan
| | - Yasuo Hosoda
- Department of Gastroenterology, National Hospital Organization Saitama Hospital, Wako, Saitama, Japan
| | | | | | - Yuki Itoi
- Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine School of Medicine Faculty of Medicine, Maebashi, Gunma, Japan
| | - Yu Hashimoto
- Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine School of Medicine Faculty of Medicine, Maebashi, Gunma, Japan
| | - Kengo Kasuga
- Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine School of Medicine Faculty of Medicine, Maebashi, Gunma, Japan
| | - Hirohito Tanaka
- Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine School of Medicine Faculty of Medicine, Maebashi, Gunma, Japan
| | - Shiko Kuribayashi
- Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine School of Medicine Faculty of Medicine, Maebashi, Gunma, Japan
| | - Yoji Takeuchi
- Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine School of Medicine Faculty of Medicine, Maebashi, Gunma, Japan
| | - Toshio Uraoka
- Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine School of Medicine Faculty of Medicine, Maebashi, Gunma, Japan
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14
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Mota J, Almeida MJ, Martins M, Mendes F, Cardoso P, Afonso J, Ribeiro T, Ferreira J, Fonseca F, Limbert M, Lopes S, Macedo G, Castro Poças F, Mascarenhas M. Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications. J Clin Med 2024; 13:5842. [PMID: 39407902 PMCID: PMC11477032 DOI: 10.3390/jcm13195842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2024] [Revised: 09/21/2024] [Accepted: 09/22/2024] [Indexed: 10/20/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a transformative tool across several specialties, namely gastroenterology, where it has the potential to optimize both diagnosis and treatment as well as enhance patient care. Coloproctology, due to its highly prevalent pathologies and tremendous potential to cause significant mortality and morbidity, has drawn a lot of attention regarding AI applications. In fact, its application has yielded impressive outcomes in various domains, colonoscopy being one prominent example, where it aids in the detection of polyps and early signs of colorectal cancer with high accuracy and efficiency. With a less explored path but equivalent promise, AI-powered capsule endoscopy ensures accurate and time-efficient video readings, already detecting a wide spectrum of anomalies. High-resolution anoscopy is an area that has been growing in interest in recent years, with efforts being made to integrate AI. There are other areas, such as functional studies, that are currently in the early stages, but evidence is expected to emerge soon. According to the current state of research, AI is anticipated to empower gastroenterologists in the decision-making process, paving the way for a more precise approach to diagnosing and treating patients. This review aims to provide the state-of-the-art use of AI in coloproctology while also reflecting on future directions and perspectives.
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Affiliation(s)
- Joana Mota
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Maria João Almeida
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, 4200-065 Porto, Portugal;
- DigestAID—Digestive Artificial Intelligence Development, Rua Alfredo Allen n.° 455/461, 4200-135 Porto, Portugal
| | - Filipa Fonseca
- Instituto Português de Oncologia de Lisboa Francisco Gentil (IPO Lisboa), 1099-023 Lisboa, Portugal; (F.F.); (M.L.)
| | - Manuel Limbert
- Instituto Português de Oncologia de Lisboa Francisco Gentil (IPO Lisboa), 1099-023 Lisboa, Portugal; (F.F.); (M.L.)
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
| | - Susana Lopes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
- Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal
| | - Fernando Castro Poças
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
- Department of Gastroenterology, Santo António University Hospital, 4099-001 Porto, Portugal
- Abel Salazar Biomedical Sciences Institute (ICBAS), 4050-313 Porto, Portugal
| | - Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
- Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal
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15
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Mei Q, Kuo P, Wei JCC. Limitations in Autonomous Artificial Intelligence Optical Diagnosis. Gastroenterology 2024:S0016-5085(24)05406-4. [PMID: 39214505 DOI: 10.1053/j.gastro.2024.08.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2024] [Accepted: 08/02/2024] [Indexed: 09/04/2024]
Affiliation(s)
- Qi Mei
- Cancer Center, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China
| | - Poi Kuo
- Chung Shan Medical University Hospital Research Department, Taichung, Taiwan
| | - James Cheng-Chung Wei
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China; Department of Allergy, Immunology and Rheumatology, Chung Shan Medical University Hospital; Institute of Medicine, Graduate Institute of Integrated Medicine, China Medical University, Taichung, Taiwan
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16
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Bou Jaoude J, Al Bacha R, Abboud B. Will artificial intelligence reach any limit in gastroenterology? Artif Intell Gastroenterol 2024; 5:91336. [DOI: 10.35712/aig.v5.i2.91336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 04/25/2024] [Accepted: 06/07/2024] [Indexed: 08/08/2024] Open
Abstract
Endoscopy is the cornerstone in the management of digestive diseases. Over the last few decades, technology has played an important role in the development of this field, helping endoscopists in better detecting and characterizing luminal lesions. However, despite ongoing advancements in endoscopic technology, the incidence of missed pre-neoplastic and neoplastic lesions remains high due to the operator-dependent nature of endoscopy and the challenging learning curve associated with new technologies. Artificial intelligence (AI), an operator-independent field, could be an invaluable solution. AI can serve as a “second observer”, enhancing the performance of endoscopists in detecting and characterizing luminal lesions. By utilizing deep learning (DL), an innovation within machine learning, AI automatically extracts input features from targeted endoscopic images. DL encompasses both computer-aided detection and computer-aided diagnosis, assisting endoscopists in reducing missed detection rates and predicting the histology of luminal digestive lesions. AI applications in clinical gastrointestinal diseases are continuously expanding and evolving the entire digestive tract. In all published studies, real-time AI assists endoscopists in improving the performance of non-expert gastroenterologists, bringing it to a level comparable to that of experts. The development of DL may be affected by selection biases. Studies have utilized different AI-assisted models, which are heterogeneous. In the future, algorithms need validation through large, randomized trials. Theoretically, AI has no limit to assist endoscopists in increasing the accuracy and the quality of endoscopic exams. However, practically, we still have a long way to go before standardizing our AI models to be accepted and applied by all gastroenterologists.
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Affiliation(s)
- Joseph Bou Jaoude
- Department of Gastroenterology, Levant Hospital, Beirut 166830, Lebanon
| | - Rose Al Bacha
- Department of Gastroenterology, Levant Hospital, Beirut 166830, Lebanon
| | - Bassam Abboud
- Department of General Surgery, Geitaoui Hospital, Faculty of Medicine, Lebanese University, Lebanon, Beirut 166830, Lebanon
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17
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Rex DK, Bhavsar-Burke I, Buckles D, Burton J, Cartee A, Comar K, Edwards A, Fennimore B, Fischer M, Gerich M, Gilmore A, Hamdeh S, Hoffman J, Ibach M, Jackson M, James-Stevenson T, Kaltenbach T, Kaplan J, Kapur S, Kohm D, Kriss M, Kundumadam S, Kyanam Kabir Baig KR, Menard-Katcher P, Kraft C, Langworthy J, Misra B, Molloy E, Munoz JC, Norvell J, Nowak T, Obaitan I, Patel S, Patel M, Peter S, Reid BM, Rogers N, Ross J, Ryan J, Sagi S, Saito A, Samo S, Sarkis F, Scott FI, Siwiec R, Sullivan S, Wieland A, Zhang J, Repici A, Hassan C, Byrne MF, Rastogi A. Artificial Intelligence for Real-Time Prediction of the Histology of Colorectal Polyps by General Endoscopists. Ann Intern Med 2024; 177:911-918. [PMID: 38768450 DOI: 10.7326/m24-0086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Real-time prediction of histologic features of small colorectal polyps may prevent resection and/or pathologic evaluation and therefore decrease colonoscopy costs. Previous studies showed that computer-aided diagnosis (CADx) was highly accurate, though it did not outperform expert endoscopists. OBJECTIVE To assess the diagnostic performance of histologic predictions by general endoscopists before and after assistance from CADx in a real-life setting. DESIGN Prospective, multicenter, single-group study. (ClinicalTrials.gov: NCT04437615). SETTING 6 centers across the United States. PARTICIPANTS 1252 consecutive patients undergoing colonoscopy and 49 general endoscopists with variable experience in real-time prediction of polyp histologic features. INTERVENTION Real-time use of CADx during routine colonoscopy. MEASUREMENTS The primary end points were the sensitivity and specificity of CADx-unassisted and CADx-assisted histologic predictions for adenomas measuring 5 mm or less. For clinical purposes, additional estimates according to location and confidence level were provided. RESULTS The CADx device made a diagnosis for 2695 polyps measuring 5 mm or less (96%) in 1252 patients. There was no difference in sensitivity between the unassisted and assisted groups (90.7% vs. 90.8%; P = 0.52). Specificity was higher in the CADx-assisted group (59.5% vs. 64.7%; P < 0.001). Among all 2695 polyps measuring 5 mm or less, 88.2% and 86.1% (P < 0.001) in the CADx-assisted and unassisted groups, respectively, could be resected and discarded without pathologic evaluation. Among 743 rectosigmoid polyps measuring 5 mm or less, 49.5% and 47.9% (P < 0.001) in the CADx-assisted and unassisted groups, respectively, could be left in situ without resection. LIMITATION Decision making based on CADx might differ outside a clinical trial. CONCLUSION CADx assistance did not result in increased sensitivity of optical diagnosis. Despite a slight increase, the specificity of CADx-assisted diagnosis remained suboptimal. PRIMARY FUNDING SOURCE Olympus America Corporation served as the clinical study sponsor.
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Affiliation(s)
- Douglas K Rex
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Indira Bhavsar-Burke
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Daniel Buckles
- University of Kansas Medical Center, Kansas City, Kansas (D.B., S.H., M.J., S.K., J.L., E.M., M.P., S.S., A.R.)
| | - James Burton
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Amanda Cartee
- University of Alabama at Birmingham, Birmingham, Alabama (A.C., A.E., K.R.K.K.B., S.P., F.S.)
| | - Kevin Comar
- Borland Groover, Jacksonville, Florida (K.C., J.H., M.I., D.K., B.M., J.C.M., B.M.R., J.R.)
| | - Adam Edwards
- University of Alabama at Birmingham, Birmingham, Alabama (A.C., A.E., K.R.K.K.B., S.P., F.S.)
| | - Blair Fennimore
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Monika Fischer
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Mark Gerich
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Ashley Gilmore
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Shadi Hamdeh
- University of Kansas Medical Center, Kansas City, Kansas (D.B., S.H., M.J., S.K., J.L., E.M., M.P., S.S., A.R.)
| | - Jeffrey Hoffman
- Borland Groover, Jacksonville, Florida (K.C., J.H., M.I., D.K., B.M., J.C.M., B.M.R., J.R.)
| | - Michael Ibach
- Borland Groover, Jacksonville, Florida (K.C., J.H., M.I., D.K., B.M., J.C.M., B.M.R., J.R.)
| | - Mollie Jackson
- University of Kansas Medical Center, Kansas City, Kansas (D.B., S.H., M.J., S.K., J.L., E.M., M.P., S.S., A.R.)
| | - Toyia James-Stevenson
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Tonya Kaltenbach
- San Francisco VA Medical Center, San Francisco, California (T.K., C.K., J.R.)
| | - Jeffrey Kaplan
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Saurabh Kapur
- University of Kansas Medical Center, Kansas City, Kansas (D.B., S.H., M.J., S.K., J.L., E.M., M.P., S.S., A.R.)
| | - Daniel Kohm
- Borland Groover, Jacksonville, Florida (K.C., J.H., M.I., D.K., B.M., J.C.M., B.M.R., J.R.)
| | - Michael Kriss
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Shanker Kundumadam
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | | | - Paul Menard-Katcher
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Cary Kraft
- San Francisco VA Medical Center, San Francisco, California (T.K., C.K., J.R.)
| | - James Langworthy
- University of Kansas Medical Center, Kansas City, Kansas (D.B., S.H., M.J., S.K., J.L., E.M., M.P., S.S., A.R.)
| | - Bharat Misra
- Borland Groover, Jacksonville, Florida (K.C., J.H., M.I., D.K., B.M., J.C.M., B.M.R., J.R.)
| | - Eric Molloy
- University of Kansas Medical Center, Kansas City, Kansas (D.B., S.H., M.J., S.K., J.L., E.M., M.P., S.S., A.R.)
| | - Juan Carlos Munoz
- Borland Groover, Jacksonville, Florida (K.C., J.H., M.I., D.K., B.M., J.C.M., B.M.R., J.R.)
| | - John Norvell
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Thomas Nowak
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Itegbemie Obaitan
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Swati Patel
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Mitesh Patel
- University of Kansas Medical Center, Kansas City, Kansas (D.B., S.H., M.J., S.K., J.L., E.M., M.P., S.S., A.R.)
| | - Shajan Peter
- University of Alabama at Birmingham, Birmingham, Alabama (A.C., A.E., K.R.K.K.B., S.P., F.S.)
| | - B Marie Reid
- Borland Groover, Jacksonville, Florida (K.C., J.H., M.I., D.K., B.M., J.C.M., B.M.R., J.R.)
| | - Nicholas Rogers
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Jason Ross
- Borland Groover, Jacksonville, Florida (K.C., J.H., M.I., D.K., B.M., J.C.M., B.M.R., J.R.)
| | - James Ryan
- San Francisco VA Medical Center, San Francisco, California (T.K., C.K., J.R.)
| | - Sashidhar Sagi
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Akira Saito
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Salih Samo
- University of Kansas Medical Center, Kansas City, Kansas (D.B., S.H., M.J., S.K., J.L., E.M., M.P., S.S., A.R.)
| | - Fayez Sarkis
- University of Alabama at Birmingham, Birmingham, Alabama (A.C., A.E., K.R.K.K.B., S.P., F.S.)
| | - Frank I Scott
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Robert Siwiec
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Shelby Sullivan
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Amanda Wieland
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Jianying Zhang
- Department of Statistics, Olympus America Corporation, Center Valley, Pennsylvania (J.Z.)
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy, and Endoscopy Unit and Humanitas Clinical and Research Hospital, IRCCS, Rozzano, Italy (A.R., C.H.)
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy, and Endoscopy Unit and Humanitas Clinical and Research Hospital, IRCCS, Rozzano, Italy (A.R., C.H.)
| | - Michael F Byrne
- Division of Gastroenterology, Vancouver General Hospital; University of British Columbia; and Satisfai Health, Vancouver, British Columbia, Canada (M.F.B.)
| | - Amit Rastogi
- University of Kansas Medical Center, Kansas City, Kansas (D.B., S.H., M.J., S.K., J.L., E.M., M.P., S.S., A.R.)
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Mori Y, Jin EH, Lee D. Enhancing artificial intelligence-doctor collaboration for computer-aided diagnosis in colonoscopy through improved digital literacy. Dig Liver Dis 2024; 56:1140-1143. [PMID: 38105144 DOI: 10.1016/j.dld.2023.11.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/16/2023] [Accepted: 11/22/2023] [Indexed: 12/19/2023]
Abstract
Establishing appropriate trust and maintaining a balanced reliance on digital resources are vital for accurate optical diagnoses and effective integration of computer-aided diagnosis (CADx) in colonoscopy. Active learning using diverse polyp image datasets can help in developing precise CADx systems. Enhancing doctors' digital literacy and interpreting their results is crucial. Explainable artificial intelligence (AI) addresses opacity, and textual descriptions, along with AI-generated content, deepen the interpretability of AI-based findings by doctors. AI conveying uncertainties and decision confidence aids doctors' acceptance of results. Optimal AI-doctor collaboration requires improving algorithm performance, transparency, addressing uncertainties, and enhancing doctors' optical diagnostic skills.
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Affiliation(s)
- Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo and Oslo University Hospital, Oslo, Norway; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway; Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Eun Hyo Jin
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea; Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, South Korea.
| | - Dongheon Lee
- Department of Biomedical Engineering, College of Medicine, Chungnam National University, Daejeon, South Korea; Department of Biomedical Engineering, Chungnam National University Hospital, Daejeon, South Korea
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19
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Spadaccini M, Troya J, Khalaf K, Facciorusso A, Maselli R, Hann A, Repici A. Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going? Dig Liver Dis 2024; 56:1148-1155. [PMID: 38458884 DOI: 10.1016/j.dld.2024.01.203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 03/10/2024]
Abstract
Colorectal cancer is a significant global health concern, necessitating effective screening strategies to reduce its incidence and mortality rates. Colonoscopy plays a crucial role in the detection and removal of colorectal neoplastic precursors. However, there are limitations and variations in the performance of endoscopists, leading to missed lesions and suboptimal outcomes. The emergence of artificial intelligence (AI) in endoscopy offers promising opportunities to improve the quality and efficacy of screening colonoscopies. In particular, AI applications, including computer-aided detection (CADe) and computer-aided characterization (CADx), have demonstrated the potential to enhance adenoma detection and optical diagnosis accuracy. Additionally, AI-assisted quality control systems aim to standardize the endoscopic examination process. This narrative review provides an overview of AI principles and discusses the current knowledge on AI-assisted endoscopy in the context of screening colonoscopies. It highlights the significant role of AI in improving lesion detection, characterization, and quality assurance during colonoscopy. However, further well-designed studies are needed to validate the clinical impact and cost-effectiveness of AI-assisted colonoscopy before its widespread implementation.
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Affiliation(s)
- Marco Spadaccini
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy.
| | - Joel Troya
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Kareem Khalaf
- Division of Gastroenterology, St. Michael's Hospital, University of Toronto, Toronto, Canada
| | - Antonio Facciorusso
- Gastroenterology Unit, Department of Surgical and Medical Sciences, University of Foggia, Foggia, Italy
| | - Roberta Maselli
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
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Mascagni P, Alapatt D, Sestini L, Yu T, Alfieri S, Morales-Conde S, Padoy N, Perretta S. Applications of artificial intelligence in surgery: clinical, technical, and governance considerations. Cir Esp 2024; 102 Suppl 1:S66-S71. [PMID: 38704146 DOI: 10.1016/j.cireng.2024.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 05/06/2024]
Abstract
Artificial intelligence (AI) will power many of the tools in the armamentarium of digital surgeons. AI methods and surgical proof-of-concept flourish, but we have yet to witness clinical translation and value. Here we exemplify the potential of AI in the care pathway of colorectal cancer patients and discuss clinical, technical, and governance considerations of major importance for the safe translation of surgical AI for the benefit of our patients and practices.
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Affiliation(s)
- Pietro Mascagni
- IHU Strasbourg, Strasbourg, France; Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Università Cattolica del Sacro Cuore, Rome, Italy.
| | - Deepak Alapatt
- University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France
| | - Luca Sestini
- University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France
| | - Tong Yu
- University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France
| | - Sergio Alfieri
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Università Cattolica del Sacro Cuore, Rome, Italy
| | | | - Nicolas Padoy
- IHU Strasbourg, Strasbourg, France; University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France
| | - Silvana Perretta
- IHU Strasbourg, Strasbourg, France; IRCAD, Research Institute Against Digestive Cancer, Strasbourg, France; Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
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21
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Hassan C, Misawa M, Rizkala T, Mori Y, Sultan S, Facciorusso A, Antonelli G, Spadaccini M, Houwen BBSL, Rondonotti E, Patel H, Khalaf K, Li JW, Fernandez GM, Bhandari P, Dekker E, Gross S, Berzin T, Vandvik PO, Correale L, Kudo SE, Sharma P, Rex DK, Repici A, Foroutan F. Computer-Aided Diagnosis for Leaving Colorectal Polyps In Situ : A Systematic Review and Meta-analysis. Ann Intern Med 2024; 177:919-928. [PMID: 38768453 DOI: 10.7326/m23-2865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Computer-aided diagnosis (CADx) allows prediction of polyp histology during colonoscopy, which may reduce unnecessary removal of nonneoplastic polyps. However, the potential benefits and harms of CADx are still unclear. PURPOSE To quantify the benefit and harm of using CADx in colonoscopy for the optical diagnosis of small (≤5-mm) rectosigmoid polyps. DATA SOURCES Medline, Embase, and Scopus were searched for articles published before 22 December 2023. STUDY SELECTION Histologically verified diagnostic accuracy studies that evaluated the real-time performance of physicians in predicting neoplastic change of small rectosigmoid polyps without or with CADx assistance during colonoscopy. DATA EXTRACTION The clinical benefit and harm were estimated on the basis of accuracy values of the endoscopist before and after CADx assistance. The certainty of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework. The outcome measure for benefit was the proportion of polyps predicted to be nonneoplastic that would avoid removal with the use of CADx. The outcome measure for harm was the proportion of neoplastic polyps that would be not resected and left in situ due to an incorrect diagnosis with the use of CADx. Histology served as the reference standard for both outcomes. DATA SYNTHESIS Ten studies, including 3620 patients with 4103 small rectosigmoid polyps, were analyzed. The studies that assessed the performance of CADx alone (9 studies; 3237 polyps) showed a sensitivity of 87.3% (95% CI, 79.2% to 92.5%) and specificity of 88.9% (CI, 81.7% to 93.5%) in predicting neoplastic change. In the studies that compared histology prediction performance before versus after CADx assistance (4 studies; 2503 polyps), there was no difference in the proportion of polyps predicted to be nonneoplastic that would avoid removal (55.4% vs. 58.4%; risk ratio [RR], 1.06 [CI, 0.96 to 1.17]; moderate-certainty evidence) or in the proportion of neoplastic polyps that would be erroneously left in situ (8.2% vs. 7.5%; RR, 0.95 [CI, 0.69 to 1.33]; moderate-certainty evidence). LIMITATION The application of optical diagnosis was only simulated, potentially altering the decision-making process of the operator. CONCLUSION Computer-aided diagnosis provided no incremental benefit or harm in the management of small rectosigmoid polyps during colonoscopy. PRIMARY FUNDING SOURCE European Commission. (PROSPERO: CRD42023402197).
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Affiliation(s)
- Cesare Hassan
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, and Humanitas Clinical and Research Center IRCCS, Endoscopy Unit, Rozzano, Italy (C.H., M.S., A.R.)
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan (M.M., S.K.)
| | - Tommy Rizkala
- Humanitas Clinical and Research Center IRCCS, Endoscopy Unit, Rozzano, Italy (T.R., L.C.)
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan; University of Oslo, Clinical Effectiveness Research Group, and Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway (Y.M.)
| | - Shahnaz Sultan
- Division of Gastroenterology, Hepatology, and Nutrition, University of Minnesota, and VA Health Care System, Minneapolis, Minnesota (S.S.)
| | - Antonio Facciorusso
- University of Foggia, Department of Medical Sciences, Section of Gastroenterology, Foggia, Italy (A.F.)
| | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli, Ariccia, and Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, Sapienza University of Rome, Rome, Italy (G.A.)
| | - Marco Spadaccini
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, and Humanitas Clinical and Research Center IRCCS, Endoscopy Unit, Rozzano, Italy (C.H., M.S., A.R.)
| | - Britt B S L Houwen
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, the Netherlands (B.B.S.L.H.)
| | | | - Harsh Patel
- Kansas City VA Medical Center, Gastroenterology and Hepatology, Kansas City, Missouri (H.P., P.S.)
| | - Kareem Khalaf
- Division of Gastroenterology, St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada (K.K.)
| | - James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, and Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore (J.W.L.)
| | - Gloria M Fernandez
- Endoscopy Unit, Gastroenterology Department, Clinical Institute of Digestive and Metabolic Disease, Hospital Clínic de Barcelona, University of Barcelona, Barcelona, Spain (G.M.F.)
| | - Pradeep Bhandari
- Queen Alexandra Hospital, Department of Gastroenterology, Portsmouth, United Kingdom (P.B.)
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, and Bergman Clinics Maag and Darm Amsterdam, Amsterdam, the Netherlands (E.D.)
| | - Seth Gross
- Department of Gastroenterology, Tisch Hospital, New York University Langone Medical Center, New York, New York (S.G.)
| | - Tyler Berzin
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts (T.B.)
| | - Per Olav Vandvik
- Department of Medicine, Lovisenberg Diaconal Hospital, Oslo, Norway (P.O.V.)
| | - Loredana Correale
- Humanitas Clinical and Research Center IRCCS, Endoscopy Unit, Rozzano, Italy (T.R., L.C.)
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan (M.M., S.K.)
| | - Prateek Sharma
- Kansas City VA Medical Center, Gastroenterology and Hepatology, Kansas City, Missouri (H.P., P.S.)
| | - Douglas K Rex
- Indiana University School of Medicine, Division of Gastroenterology, Indianapolis, Indiana (D.K.R.)
| | - Alessandro Repici
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, and Humanitas Clinical and Research Center IRCCS, Endoscopy Unit, Rozzano, Italy (C.H., M.S., A.R.)
| | - Farid Foroutan
- Ted Rogers Centre for Heart Research, University Health Network, Toronto, Ontario, Canada (F.F.)
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Djinbachian R, Haumesser C, Taghiakbari M, Pohl H, Barkun A, Sidani S, Liu Chen Kiow J, Panzini B, Bouchard S, Deslandres E, Alj A, von Renteln D. Autonomous Artificial Intelligence vs Artificial Intelligence-Assisted Human Optical Diagnosis of Colorectal Polyps: A Randomized Controlled Trial. Gastroenterology 2024; 167:392-399.e2. [PMID: 38331204 DOI: 10.1053/j.gastro.2024.01.044] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/15/2024] [Accepted: 01/30/2024] [Indexed: 02/10/2024]
Abstract
BACKGROUND & AIMS Artificial intelligence (AI)-based optical diagnosis systems (CADx) have been developed to allow pathology prediction of colorectal polyps during colonoscopies. However, CADx systems have not yet been validated for autonomous performance. Therefore, we conducted a trial comparing autonomous AI to AI-assisted human (AI-H) optical diagnosis. METHODS We performed a randomized noninferiority trial of patients undergoing elective colonoscopies at 1 academic institution. Patients were randomized into (1) autonomous AI-based CADx optical diagnosis of diminutive polyps without human input or (2) diagnosis by endoscopists who performed optical diagnosis of diminutive polyps after seeing the real-time CADx diagnosis. The primary outcome was accuracy in optical diagnosis in both arms using pathology as the gold standard. Secondary outcomes included agreement with pathology for surveillance intervals. RESULTS A total of 467 patients were randomized (238 patients/158 polyps in the autonomous AI group and 229 patients/179 polyps in the AI-H group). Accuracy for optical diagnosis was 77.2% (95% confidence interval [CI], 69.7-84.7) in the autonomous AI group and 72.1% (95% CI, 65.5-78.6) in the AI-H group (P = .86). For high-confidence diagnoses, accuracy for optical diagnosis was 77.2% (95% CI, 69.7-84.7) in the autonomous AI group and 75.5% (95% CI, 67.9-82.0) in the AI-H group. Autonomous AI had statistically significantly higher agreement with pathology-based surveillance intervals compared to AI-H (91.5% [95% CI, 86.9-96.1] vs 82.1% [95% CI, 76.5-87.7]; P = .016). CONCLUSIONS Autonomous AI-based optical diagnosis exhibits noninferior accuracy to endoscopist-based diagnosis. Both autonomous AI and AI-H exhibited relatively low accuracy for optical diagnosis; however, autonomous AI achieved higher agreement with pathology-based surveillance intervals. (ClinicalTrials.gov, Number NCT05236790).
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Affiliation(s)
- Roupen Djinbachian
- Montreal University Hospital Research Center, Montreal, Quebec, Canada; Division of Gastroenterology, Montreal University Hospital Center, Montreal, Quebec, Canada
| | - Claire Haumesser
- Montreal University Hospital Research Center, Montreal, Quebec, Canada
| | - Mahsa Taghiakbari
- Montreal University Hospital Research Center, Montreal, Quebec, Canada; Division of Gastroenterology, Montreal University Hospital Center, Montreal, Quebec, Canada
| | - Heiko Pohl
- Section of Gastroenterology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire; Department of Gastroenterology, Veterans Affairs White River Junction, Vermont
| | - Alan Barkun
- Division of Gastroenterology, McGill University and McGill University Health Center, Montreal, Quebec, Canada
| | - Sacha Sidani
- Division of Gastroenterology, Montreal University Hospital Center, Montreal, Quebec, Canada
| | - Jeremy Liu Chen Kiow
- Division of Gastroenterology, Montreal University Hospital Center, Montreal, Quebec, Canada
| | - Benoit Panzini
- Division of Gastroenterology, Montreal University Hospital Center, Montreal, Quebec, Canada
| | - Simon Bouchard
- Division of Gastroenterology, Montreal University Hospital Center, Montreal, Quebec, Canada
| | - Erik Deslandres
- Division of Gastroenterology, Montreal University Hospital Center, Montreal, Quebec, Canada
| | - Abla Alj
- Division of Internal Medicine, Montreal University Hospital Center, Montreal, Quebec, Canada
| | - Daniel von Renteln
- Montreal University Hospital Research Center, Montreal, Quebec, Canada; Division of Gastroenterology, Montreal University Hospital Center, Montreal, Quebec, Canada.
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23
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Lingam G, Shakir T, Kader R, Chand M. Role of artificial intelligence in colorectal cancer. Artif Intell Gastrointest Endosc 2024; 5:90723. [DOI: 10.37126/aige.v5.i2.90723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 04/10/2024] [Accepted: 04/19/2024] [Indexed: 05/11/2024] Open
Abstract
The sphere of artificial intelligence (AI) is ever expanding. Applications for clinical practice have been emerging over recent years. Although its uptake has been most prominent in endoscopy, this represents only one aspect of holistic patient care. There are a multitude of other potential avenues in which gastrointestinal care may be involved. We aim to review the role of AI in colorectal cancer as a whole. We performed broad scoping and focused searches of the applications of AI in the field of colorectal cancer. All trials including qualitative research were included from the year 2000 onwards. Studies were grouped into pre-operative, intra-operative and post-operative aspects. Pre-operatively, the major use is with endoscopic recognition. Colonoscopy has embraced the use for human derived classifications such as Narrow-band Imaging International Colorectal Endoscopic, Japan Narrow-band Imaging Expert Team, Paris and Kudo. However, novel detection and diagnostic methods have arisen from advances in AI classification. Intra-operatively, adjuncts such as image enhanced identification of structures and assessment of perfusion have led to improvements in clinical outcomes. Post-operatively, monitoring and surveillance have taken strides with potential socioeconomic and environmental savings. The uses of AI within the umbrella of colorectal surgery are multiple. We have identified existing technologies which are already augmenting cancer care. The future applications are exciting and could at least match, if not surpass human standards.
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Affiliation(s)
- Gita Lingam
- Department of General Surgery, Princess Alexandra Hospital, Harlow CM20 1QX, United Kingdom
| | - Taner Shakir
- Department of Colorectal Surgery, University College London, London W1W 7TY, United Kingdom
| | - Rawen Kader
- Department of Gastroenterology, University College London, University College London Hospitals Nhs Foundation Trust, London W1B, United Kingdom
| | - Manish Chand
- Gastroenterological Intervention Centre, University College London, London W1W 7TS, United Kingdom
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Elamin S, Duffourc M, Berzin TM, Geissler ME, Gerke S. Artificial Intelligence and Medical Liability in Gastrointestinal Endoscopy. Clin Gastroenterol Hepatol 2024; 22:1165-1169.e1. [PMID: 38614138 DOI: 10.1016/j.cgh.2024.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/15/2024]
Affiliation(s)
- Sami Elamin
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.
| | - Mindy Duffourc
- Maastricht University, Faculty of Law, Maastricht, Netherlands
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Mark E Geissler
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Sara Gerke
- Penn State Dickinson Law, Carlisle, Pennsylvania
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25
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Biffi C, Antonelli G, Bernhofer S, Hassan C, Hirata D, Iwatate M, Maieron A, Salvagnini P, Cherubini A. REAL-Colon: A dataset for developing real-world AI applications in colonoscopy. Sci Data 2024; 11:539. [PMID: 38796533 PMCID: PMC11127922 DOI: 10.1038/s41597-024-03359-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 05/10/2024] [Indexed: 05/28/2024] Open
Abstract
Detection and diagnosis of colon polyps are key to preventing colorectal cancer. Recent evidence suggests that AI-based computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems can enhance endoscopists' performance and boost colonoscopy effectiveness. However, most available public datasets primarily consist of still images or video clips, often at a down-sampled resolution, and do not accurately represent real-world colonoscopy procedures. We introduce the REAL-Colon (Real-world multi-center Endoscopy Annotated video Library) dataset: a compilation of 2.7 M native video frames from sixty full-resolution, real-world colonoscopy recordings across multiple centers. The dataset contains 350k bounding-box annotations, each created under the supervision of expert gastroenterologists. Comprehensive patient clinical data, colonoscopy acquisition information, and polyp histopathological information are also included in each video. With its unprecedented size, quality, and heterogeneity, the REAL-Colon dataset is a unique resource for researchers and developers aiming to advance AI research in colonoscopy. Its openness and transparency facilitate rigorous and reproducible research, fostering the development and benchmarking of more accurate and reliable colonoscopy-related algorithms and models.
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Affiliation(s)
- Carlo Biffi
- Cosmo Intelligent Medical Devices, Dublin, Ireland.
| | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli (N.O.C.), Rome, Italy
| | - Sebastian Bernhofer
- Karl Landsteiner University of Health Sciences, Krems, Austria
- Department of Internal Medicine 2, University Hospital St. Pölten, St. Pölten, Austria
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy
| | - Daizen Hirata
- Gastrointestinal Center, Sano Hospital, Hyogo, Japan
| | - Mineo Iwatate
- Gastrointestinal Center, Sano Hospital, Hyogo, Japan
| | - Andreas Maieron
- Karl Landsteiner University of Health Sciences, Krems, Austria
- Department of Internal Medicine 2, University Hospital St. Pölten, St. Pölten, Austria
| | | | - Andrea Cherubini
- Cosmo Intelligent Medical Devices, Dublin, Ireland.
- Milan Center for Neuroscience, University of Milano-Bicocca, Milano, Italy.
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Rondonotti E, Bergna IMB, Paggi S, Amato A, Andrealli A, Scardino G, Tamanini G, Lenoci N, Mandelli G, Terreni N, Rocchetto SI, Piagnani A, Di Paolo D, Bina N, Filippi E, Ambrosiani L, Hassan C, Correale L, Radaelli F. White light computer-aided optical diagnosis of diminutive colorectal polyps in routine clinical practice. Endosc Int Open 2024; 12:E676-E683. [PMID: 38774861 PMCID: PMC11108657 DOI: 10.1055/a-2303-0922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 04/04/2024] [Indexed: 05/24/2024] Open
Abstract
Background and study aims Artificial Intelligence (AI) systems could make the optical diagnosis (OD) of diminutive colorectal polyps (DCPs) more reliable and objective. This study was aimed at prospectively evaluating feasibility and diagnostic performance of AI-standalone and AI-assisted OD of DCPs in a real-life setting by using a white light-based system (GI Genius, Medtronic Co, Minneapolis, Minnesota, United States). Patients and methods Consecutive colonoscopy outpatients with at least one DCP were evaluated by 11 endoscopists (5 experts and 6 non-experts in OD). DCPs were classified in real time by AI (AI-standalone OD) and by the endoscopist with the assistance of AI (AI-assisted OD), with histopathology as the reference standard. Results Of the 480 DCPs, AI provided the outcome "adenoma" or "non-adenoma" in 81.4% (95% confidence interval [CI]: 77.5-84.6). Sensitivity, specificity, positive and negative predictive value, and accuracy of AI-standalone OD were 97.0% (95% CI 94.0-98.6), 38.1% (95% CI 28.9-48.1), 80.1% (95% CI 75.2-84.2), 83.3% (95% CI 69.2-92.0), and 80.5% (95% CI 68.7-82.8%), respectively. Compared with AI-standalone, the specificity of AI-assisted OD was significantly higher (58.9%, 95% CI 49.7-67.5) and a trend toward an increase was observed for other diagnostic performance measures. Overall accuracy and negative predictive value of AI-assisted OD for experts and non-experts were 85.8% (95% CI 80.0-90.4) vs. 80.1% (95% CI 73.6-85.6) and 89.1% (95% CI 75.6-95.9) vs. 80.0% (95% CI 63.9-90.4), respectively. Conclusions Standalone AI is able to provide an OD of adenoma/non-adenoma in more than 80% of DCPs, with a high sensitivity but low specificity. The human-machine interaction improved diagnostic performance, especially when experts were involved.
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Affiliation(s)
| | - Irene Maria Bambina Bergna
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- University of Milan, Milano, Italy
- Gastroenterology and Digestive Endoscopy Unit, Alessandro Manzoni Hospital, Lecco, Italy
| | - Silvia Paggi
- Gastroenterology Unit, Valduce Hospital, Como, Italy
| | - Arnaldo Amato
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Gastroenterology and Digestive Endoscopy Unit, Alessandro Manzoni Hospital, Lecco, Italy
| | | | | | | | | | | | | | - SImone Rocchetto
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- University of Milan, Milano, Italy
| | - Alessandra Piagnani
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- University of Milan, Milano, Italy
| | | | - Niccolò Bina
- Gastroenterology Unit, Valduce Hospital, Como, Italy
| | | | | | - Cesare Hassan
- Department of Gastroenterology, IRCCS Humanitas Research Hospital, Rozzano, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Loredana Correale
- Department of Gastroenterology, IRCCS Humanitas Research Hospital, Rozzano, Italy
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Palomba G, Fernicola A, Corte MD, Capuano M, De Palma GD, Aprea G. Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review. AIMS Public Health 2024; 11:557-576. [PMID: 39027395 PMCID: PMC11252578 DOI: 10.3934/publichealth.2024028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/29/2024] [Accepted: 04/02/2024] [Indexed: 07/20/2024] Open
Abstract
Artificial intelligence (AI) is playing an increasing role in several fields of medicine. It is also gaining popularity among surgeons as a valuable screening and diagnostic tool for many conditions such as benign and malignant colorectal, gastric, thyroid, parathyroid, and breast disorders. In the literature, there is no review that groups together the various application domains of AI when it comes to the screening and diagnosis of main surgical diseases. The aim of this review is to describe the use of AI in these settings. We performed a literature review by searching PubMed, Web of Science, Scopus, and Embase for all studies investigating the role of AI in the surgical setting, published between January 01, 2000, and June 30, 2023. Our focus was on randomized controlled trials (RCTs), meta-analysis, systematic reviews, and observational studies, dealing with large cohorts of patients. We then gathered further relevant studies from the reference list of the selected publications. Based on the studies reviewed, it emerges that AI could strongly enhance the screening efficiency, clinical ability, and diagnostic accuracy for several surgical conditions. Some of the future advantages of this technology include implementing, speeding up, and improving the automaticity with which AI recognizes, differentiates, and classifies the various conditions.
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Affiliation(s)
- Giuseppe Palomba
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Agostino Fernicola
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Marcello Della Corte
- Azienda Ospedaliera Universitaria San Giovanni di Dio e Ruggi d'Aragona - OO. RR. Scuola Medica Salernitana, Salerno, Italy
| | - Marianna Capuano
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Giovanni Domenico De Palma
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Giovanni Aprea
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
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28
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van der Zander QEW, Schreuder RM, Thijssen A, Kusters CHJ, Dehghani N, Scheeve T, Winkens B, van der Ende - van Loon MCM, de With PHN, van der Sommen F, Masclee AAM, Schoon EJ. Artificial intelligence for characterization of diminutive colorectal polyps: A feasibility study comparing two computer-aided diagnosis systems. Artif Intell Gastrointest Endosc 2024; 5:90574. [DOI: 10.37126/aige.v5.i1.90574] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/11/2024] [Accepted: 02/02/2024] [Indexed: 02/20/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has potential in the optical diagnosis of colorectal polyps.
AIM To evaluate the feasibility of the real-time use of the computer-aided diagnosis system (CADx) AI for ColoRectal Polyps (AI4CRP) for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYETM (Fujifilm, Tokyo, Japan). CADx influence on the optical diagnosis of an expert endoscopist was also investigated.
METHODS AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm. Both CADx-systems exploit convolutional neural networks. Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard. AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value (range 0.0-1.0). A predefined cut-off value of 0.6 was set with values < 0.6 indicating benign and values ≥ 0.6 indicating premalignant colorectal polyps. Low confidence characterizations were defined as values 40% around the cut-off value of 0.6 (< 0.36 and > 0.76). Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.
RESULTS AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps. Self-critical AI4CRP, excluding 14 low confidence characterizations [27.5% (14/51)], had a diagnostic accuracy of 89.2%, sensitivity of 89.7%, and specificity of 87.5%, which was higher compared to AI4CRP. CAD EYE had a 83.7% diagnostic accuracy, 74.2% sensitivity, and 100.0% specificity. Diagnostic performances of the endoscopist alone (before AI) increased non-significantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE (AI-assisted endoscopist). Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems, except for specificity for which CAD EYE performed best.
CONCLUSION Real-time use of AI4CRP was feasible. Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.
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Affiliation(s)
- Quirine Eunice Wennie van der Zander
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht 6202 AZ, Netherlands
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht 6200 MD, Netherlands
| | - Ramon M Schreuder
- Division of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven 5602 ZA, Netherlands
| | - Ayla Thijssen
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht 6202 AZ, Netherlands
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht 6200 MD, Netherlands
| | - Carolus H J Kusters
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Nikoo Dehghani
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Thom Scheeve
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Bjorn Winkens
- Department of Methodology and Statistics, Maastricht University, Postbus 616, 6200 MD Maastricht, Netherlands
- School for Public Health and Primary Care, Maastricht University, Maastricht 6200 MD, Netherlands
| | | | - Peter H N de With
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Ad A M Masclee
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht 6202 AZ, Netherlands
| | - Erik J Schoon
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht 6200 MD, Netherlands
- Division of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven 5602 ZA, Netherlands
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29
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Kato S, Kudo SE, Minegishi Y, Miyata Y, Maeda Y, Kuroki T, Takashina Y, Mochizuki K, Tamura E, Abe M, Sato Y, Sakurai T, Kouyama Y, Tanaka K, Ogawa Y, Nakamura H, Ichimasa K, Ogata N, Hisayuki T, Hayashi T, Wakamura K, Miyachi H, Baba T, Ishida F, Nemoto T, Misawa M. Impact of computer-aided characterization for diagnosis of colorectal lesions, including sessile serrated lesions: Multireader, multicase study. Dig Endosc 2024; 36:341-350. [PMID: 37937532 DOI: 10.1111/den.14612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 06/06/2023] [Indexed: 11/09/2023]
Abstract
OBJECTIVES Computer-aided characterization (CADx) may be used to implement optical biopsy strategies into colonoscopy practice; however, its impact on endoscopic diagnosis remains unknown. We aimed to evaluate the additional diagnostic value of CADx when used by endoscopists for assessing colorectal polyps. METHODS This was a single-center, multicase, multireader, image-reading study using randomly extracted images of pathologically confirmed polyps resected between July 2021 and January 2022. Approved CADx that could predict two-tier classification (neoplastic or nonneoplastic) by analyzing narrow-band images of the polyps was used to obtain a CADx diagnosis. Participating endoscopists determined if the polyps were neoplastic or not and noted their confidence level using a computer-based, image-reading test. The test was conducted twice with a 4-week interval: the first test was conducted without CADx prediction and the second test with CADx prediction. Diagnostic performances for neoplasms were calculated using the pathological diagnosis as reference and performances with and without CADx prediction were compared. RESULTS Five hundred polyps were randomly extracted from 385 patients and diagnosed by 14 endoscopists (including seven experts). The sensitivity for neoplasia was significantly improved by referring to CADx (89.4% vs. 95.6%). CADx also had incremental effects on the negative predictive value (69.3% vs. 84.3%), overall accuracy (87.2% vs. 91.8%), and high-confidence diagnosis rate (77.4% vs. 85.8%). However, there was no significant difference in specificity (80.1% vs. 78.9%). CONCLUSIONS Computer-aided characterization has added diagnostic value for differentiating colorectal neoplasms and may improve the high-confidence diagnosis rate.
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Affiliation(s)
- Shun Kato
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yosuke Minegishi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yuki Miyata
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yasuharu Maeda
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Takanori Kuroki
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yuki Takashina
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Kenichi Mochizuki
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Eri Tamura
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Masahiro Abe
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yuta Sato
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Tatsuya Sakurai
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yuta Kouyama
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Kenta Tanaka
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yushi Ogawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Hiroki Nakamura
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Katsuro Ichimasa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
- Department of Gastroenterology and Hepatology, National University Hospital, Singapore City, Singapore
| | - Noriyuki Ogata
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Tomokazu Hisayuki
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Takemasa Hayashi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Kunihiko Wakamura
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Hideyuki Miyachi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Toshiyuki Baba
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Fumio Ishida
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Tetsuo Nemoto
- Department of Diagnostic Pathology and Laboratory Medicine, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
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Khalaf K, Fujiyoshi MRA, Spadaccini M, Rizkala T, Ramai D, Colombo M, Fugazza A, Facciorusso A, Carrara S, Hassan C, Repici A. From Staining Techniques to Artificial Intelligence: A Review of Colorectal Polyps Characterization. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:89. [PMID: 38256350 PMCID: PMC10818333 DOI: 10.3390/medicina60010089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/24/2023] [Accepted: 12/26/2023] [Indexed: 01/24/2024]
Abstract
This review article provides a comprehensive overview of the evolving techniques in image-enhanced endoscopy (IEE) for the characterization of colorectal polyps, and the potential of artificial intelligence (AI) in revolutionizing the diagnostic accuracy of endoscopy. We discuss the historical use of dye-spray and virtual chromoendoscopy for the characterization of colorectal polyps, which are now being replaced with more advanced technologies. Specifically, we focus on the application of AI to create a "virtual biopsy" for the detection and characterization of colorectal polyps, with potential for replacing histopathological diagnosis. The incorporation of AI has the potential to provide an evolutionary learning system that aids in the diagnosis and management of patients with the best possible outcomes. A detailed analysis of the literature supporting AI-assisted diagnostic techniques for the detection and characterization of colorectal polyps, with a particular emphasis on AI's characterization mechanism, is provided. The benefits of AI over traditional IEE techniques, including the reduction in human error in diagnosis, and its potential to provide an accurate diagnosis with similar accuracy to the gold standard are presented. However, the need for large-scale testing of AI in clinical practice and the importance of integrating patient data into the diagnostic process are acknowledged. In conclusion, the constant evolution of IEE technology and the potential for AI to revolutionize the field of endoscopy in the future are presented.
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Affiliation(s)
- Kareem Khalaf
- Division of Gastroenterology, St. Michael’s Hospital, University of Toronto, Toronto, ON M5B 1T8, Canada; (K.K.); (M.R.A.F.)
| | - Mary Raina Angeli Fujiyoshi
- Division of Gastroenterology, St. Michael’s Hospital, University of Toronto, Toronto, ON M5B 1T8, Canada; (K.K.); (M.R.A.F.)
- Digestive Diseases Center, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | - Marco Spadaccini
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; (T.R.); (M.C.); (A.F.); (S.C.); (C.H.); (A.R.)
- Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
| | - Tommy Rizkala
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; (T.R.); (M.C.); (A.F.); (S.C.); (C.H.); (A.R.)
- Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
| | - Daryl Ramai
- Gastroenterology and Hepatology, University of Utah Health, Salt Lake City, UT 84132, USA;
| | - Matteo Colombo
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; (T.R.); (M.C.); (A.F.); (S.C.); (C.H.); (A.R.)
| | - Alessandro Fugazza
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; (T.R.); (M.C.); (A.F.); (S.C.); (C.H.); (A.R.)
| | - Antonio Facciorusso
- Department of Endoscopy, Section of Gastroenterology, Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy;
| | - Silvia Carrara
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; (T.R.); (M.C.); (A.F.); (S.C.); (C.H.); (A.R.)
| | - Cesare Hassan
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; (T.R.); (M.C.); (A.F.); (S.C.); (C.H.); (A.R.)
- Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; (T.R.); (M.C.); (A.F.); (S.C.); (C.H.); (A.R.)
- Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
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31
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Gadi SR, Muralidharan SS, Glissen Brown JR. Colonoscopy Quality, Innovation, and the Assessment of New Technology. TECHNIQUES AND INNOVATIONS IN GASTROINTESTINAL ENDOSCOPY 2024; 26:177-192. [DOI: 10.1016/j.tige.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
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32
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Ueda T, Li JW, Ho SH, Singh R, Uedo N. Precision endoscopy in the era of climate change and sustainability. J Gastroenterol Hepatol 2024; 39:18-27. [PMID: 37881033 DOI: 10.1111/jgh.16383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/05/2023] [Accepted: 10/07/2023] [Indexed: 10/27/2023]
Abstract
Global warming caused by increased greenhouse gas (GHG) emissions has a direct impact on human health. Gastrointestinal (GI) endoscopy contributes significantly to GHG emissions due to energy consumption, reprocessing of endoscopes and accessories, production of equipment, safe disposal of biohazardous waste, and travel by patients. Moreover, GHGs are also generated in histopathology through tissue processing and the production of biopsy specimen bottles. The reduction in unnecessary surveillance endoscopies and biopsies is a practical approach to decrease GHG emissions without affecting disease outcomes. This narrative review explores the role of precision medicine in GI endoscopy, such as image-enhanced endoscopy and artificial intelligence, with a focus on decreasing unnecessary endoscopic procedures and biopsies in the surveillance and diagnosis of premalignant lesions in the esophagus, stomach, and colon. This review offers strategies to minimize unnecessary endoscopic procedures and biopsies, decrease GHG emissions, and maintain high-quality patient care, thereby contributing to sustainable healthcare practices.
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Affiliation(s)
- Tomoya Ueda
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - James Weiquan Li
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore, Singapore
| | - Shiaw-Hooi Ho
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Rajvinder Singh
- Department of Gastroenterology, Lyell McEwin and Modbury Hospitals, University of Adelaide, Adelaide, Australia
| | - Noriya Uedo
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
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Samarasena J, Yang D, Berzin TM. AGA Clinical Practice Update on the Role of Artificial Intelligence in Colon Polyp Diagnosis and Management: Commentary. Gastroenterology 2023; 165:1568-1573. [PMID: 37855759 DOI: 10.1053/j.gastro.2023.07.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 06/06/2023] [Accepted: 07/17/2023] [Indexed: 10/20/2023]
Abstract
DESCRIPTION The purpose of this American Gastroenterological Association (AGA) Institute Clinical Practice Update (CPU) is to review the available evidence and provide expert commentary on the current landscape of artificial intelligence in the evaluation and management of colorectal polyps. METHODS This CPU was commissioned and approved by the AGA Institute Clinical Practice Updates Committee (CPUC) and the AGA Governing Board to provide timely guidance on a topic of high clinical importance to the AGA membership and underwent internal peer review by the CPUC and external peer review through standard procedures of Gastroenterology. This Expert Commentary incorporates important as well as recently published studies in this field, and it reflects the experiences of the authors who are experienced endoscopists with expertise in the field of artificial intelligence and colorectal polyps.
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Affiliation(s)
- Jason Samarasena
- Division of Gastroenterology, University of California Irvine, Orange, California
| | - Dennis Yang
- Center for Interventional Endoscopy, AdventHealth, Orlando, Florida.
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
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Zhang C, Xu J, Tang R, Yang J, Wang W, Yu X, Shi S. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment. J Hematol Oncol 2023; 16:114. [PMID: 38012673 PMCID: PMC10680201 DOI: 10.1186/s13045-023-01514-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/20/2023] [Indexed: 11/29/2023] Open
Abstract
Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.
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Affiliation(s)
- Chaoyi Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jin Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Rong Tang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jianhui Yang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Wei Wang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
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Hsu WF, Chiu HM. Optimization of colonoscopy quality: Comprehensive review of the literature and future perspectives. Dig Endosc 2023; 35:822-834. [PMID: 37381701 DOI: 10.1111/den.14627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 06/27/2023] [Indexed: 06/30/2023]
Abstract
Colonoscopy is crucial in preventing colorectal cancer (CRC) and reducing associated mortality. This comprehensive review examines the importance of high-quality colonoscopy and associated quality indicators, including bowel preparation, cecal intubation rate, withdrawal time, adenoma detection rate (ADR), complete resection, specimen retrieval, complication rates, and patient satisfaction, while also discussing other ADR-related metrics. Additionally, the review draws attention to often overlooked quality aspects, such as nonpolypoid lesion detection, as well as insertion and withdrawal skills. Moreover, it explores the potential of artificial intelligence in enhancing colonoscopy quality and highlights specific considerations for organized screening programs. The review also emphasizes the implications of organized screening programs and the need for continuous quality improvement. A high-quality colonoscopy is crucial for preventing postcolonoscopy CRC- and CRC-related deaths. Health-care professionals must develop a thorough understanding of colonoscopy quality components, including technical quality, patient safety, and patient experience. By prioritizing ongoing evaluation and refinement of these quality indicators, health-care providers can contribute to improved patient outcomes and develop more effective CRC screening programs.
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Affiliation(s)
- Wen-Feng Hsu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Han-Mo Chiu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
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Ding M, Yan J, Chao G, Zhang S. Application of artificial intelligence in colorectal cancer screening by colonoscopy: Future prospects (Review). Oncol Rep 2023; 50:199. [PMID: 37772392 DOI: 10.3892/or.2023.8636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 07/07/2023] [Indexed: 09/30/2023] Open
Abstract
Colorectal cancer (CRC) has become a severe global health concern, with the third‑high incidence and second‑high mortality rate of all cancers. The burden of CRC is expected to surge to 60% by 2030. Fortunately, effective early evidence‑based screening could significantly reduce the incidence and mortality of CRC. Colonoscopy is the core screening method for CRC with high popularity and accuracy. Yet, the accuracy of colonoscopy in CRC screening is related to the experience and state of operating physicians. It is challenging to maintain the high CRC diagnostic rate of colonoscopy. Artificial intelligence (AI)‑assisted colonoscopy will compensate for the above shortcomings and improve the accuracy, efficiency, and quality of colonoscopy screening. The unique advantages of AI, such as the continuous advancement of high‑performance computing capabilities and innovative deep‑learning architectures, which hugely impact the control of colorectal cancer morbidity and mortality expectancy, highlight its role in colonoscopy screening.
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Affiliation(s)
- Menglu Ding
- The Second Affiliated Hospital of Zhejiang Chinese Medical University (The Xin Hua Hospital of Zhejiang Province), Hangzhou, Zhejiang 310000, P.R. China
| | - Junbin Yan
- The Second Affiliated Hospital of Zhejiang Chinese Medical University (The Xin Hua Hospital of Zhejiang Province), Hangzhou, Zhejiang 310000, P.R. China
| | - Guanqun Chao
- Department of General Practice, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, P.R. China
| | - Shuo Zhang
- The Second Affiliated Hospital of Zhejiang Chinese Medical University (The Xin Hua Hospital of Zhejiang Province), Hangzhou, Zhejiang 310000, P.R. China
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Tham S, Koh FH, Ladlad J, Chue KM, Lin CL, Teo EK, Foo FJ. The imitation game: a review of the use of artificial intelligence in colonoscopy, and endoscopists' perceptions thereof. Ann Coloproctol 2023; 39:385-394. [PMID: 36907170 PMCID: PMC10626328 DOI: 10.3393/ac.2022.00878.0125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 12/22/2022] [Accepted: 01/09/2023] [Indexed: 03/14/2023] Open
Abstract
The development of deep learning systems in artificial intelligence (AI) has enabled advances in endoscopy, and AI-aided colonoscopy has recently been ushered into clinical practice as a clinical decision-support tool. This has enabled real-time AI-aided detection of polyps with a higher sensitivity than the average endoscopist, and evidence to support its use has been promising thus far. This review article provides a summary of currently published data relating to AI-aided colonoscopy, discusses current clinical applications, and introduces ongoing research directions. We also explore endoscopists' perceptions and attitudes toward the use of this technology, and discuss factors influencing its uptake in clinical practice.
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Affiliation(s)
- Sarah Tham
- Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore
| | - Frederick H. Koh
- Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
| | - Jasmine Ladlad
- Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
| | - Koy-Min Chue
- Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
| | - SKH Endoscopy Centre
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
| | - Cui-Li Lin
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
- Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth Services, Singapore
| | - Eng-Kiong Teo
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
- Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth Services, Singapore
| | - Fung-Joon Foo
- Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
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Sung JJY, Savulescu J, Ngiam KY, An B, Ang TL, Yeoh KG, Cham TJ, Tsao S, Chua TS. Artificial intelligence for gastroenterology: Singapore artificial intelligence for Gastroenterology Working Group Position Statement. J Gastroenterol Hepatol 2023; 38:1669-1676. [PMID: 37277693 DOI: 10.1111/jgh.16241] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 06/07/2023]
Abstract
BACKGROUND Successful implementation of artificial intelligence in gastroenterology and hepatology practice requires more than technology. There are ethical, legal, and social issues that need to be settled. AIM A group consisting of AI developers (engineer), AI users (gastroenterologist, hepatologist, and surgeon) and AI regulators (ethicist and administrator) formed a Working Group to draft these Positions Statements with the objective of arousing public and professional interest and dialogue, to promote ethical considerations when implementing AI technology, to suggest to policy makers and health authorities relevant factors to take into account when approving and regulating the use of AI tools, and to engage the profession in preparing for change in clinical practice. STATEMENTS These series of Position Statements point out the salient issues to maintain the trust between care provider and care receivers, and to legitimize the use of a non-human tool in healthcare delivery. It is based on fundamental principles such as respect, autonomy, privacy, responsibility, and justice. Enforcing the use of AI without considering these factor risk damaging the doctor-patient relationship.
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Affiliation(s)
- Joseph J Y Sung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Julian Savulescu
- Centre for Biomedical Ethics, National University of Singapore, Singapore
| | - K Y Ngiam
- Department of Surgery, National University Hospital, Singapore
| | - Bo An
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
| | - Tiing Leong Ang
- Singapore Health Service, Changi General Hospital, Singapore
| | - K G Yeoh
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Gastroenterology and Hepatology, National University Hospital, National University Health System, Singapore
| | - Tat-Jen Cham
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
| | - Stephen Tsao
- National Healthcare Group, Tan Tock Seng Hospital Singapore, Singapore
- Gastroenterological Society of Singapore, Singapore
| | - T S Chua
- Gastroenterology Chapter, Academy of Medicine, Singapore
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Hassan C, Mori Y, Sharma P. The Pros and Cons of Artificial Intelligence in Endoscopy. Am J Gastroenterol 2023; 118:1720-1722. [PMID: 37052360 DOI: 10.14309/ajg.0000000000002287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 03/27/2023] [Indexed: 04/14/2023]
Affiliation(s)
- Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Endoscopy Unit, Humanitas Clinical and Research Center, IRCCS, Rozzano, Italy
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Prateek Sharma
- University of Kansas School of Medicine and VA Medical Center, Kansas City, Kansas, USA
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Kim J, Lim SH, Kang HY, Song JH, Yang SY, Chung GE, Jin EH, Choi JM, Bae JH. Impact of 3-second rule for high confidence assignment on the performance of endoscopists for the real-time optical diagnosis of colorectal polyps. Endoscopy 2023; 55:945-951. [PMID: 37172938 DOI: 10.1055/a-2073-3411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
BACKGROUND Confusion between high and low confidence decisions in optical diagnosis hinders the implementation of real-time optical diagnosis in clinical practice. We evaluated the effect of a 3-second rule (decision time limited to 3 seconds for a high confidence assignment) in expert and nonexpert endoscopists. METHODS This single-center prospective study included eight board-certified gastroenterologists. A 2-month baseline phase used standard real-time optical diagnosis for colorectal polyps < 10 mm and was followed by a 6-month intervention phase using optical diagnosis with the 3-second rule. Performance, including high confidence accuracy, and Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI) and Simple Optical Diagnosis Accuracy (SODA) thresholds, was measured. RESULTS Real-time optical diagnosis was performed on 1793 patients with 3694 polyps. There was significant improvement in high confidence accuracy between baseline and intervention phases in the nonexpert group (79.2 % vs. 86.3 %; P = 0.01) but not in the expert group (85.3 % vs. 87.5 %; P = 0.53). Using the 3-second rule improved the overall performance of PIVI and SODA in both groups. CONCLUSIONS The 3-second rule was effective in improving real-time optical diagnosis performance, especially in nonexperts.
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Affiliation(s)
- Jung Kim
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Seon Hee Lim
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Hae Yeon Kang
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Ji Hyun Song
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Sun Young Yang
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Goh Eun Chung
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Eun Hyo Jin
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Ji Min Choi
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Jung Ho Bae
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
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Baumer S, Streicher K, Alqahtani SA, Brookman-Amissah D, Brunner M, Federle C, Muehlenberg K, Pfeifer L, Salzberger A, Schorr W, Zustin J, Pech O. Accuracy of polyp characterization by artificial intelligence and endoscopists: a prospective, non-randomized study in a tertiary endoscopy center. Endosc Int Open 2023; 11:E818-E828. [PMID: 37727511 PMCID: PMC10506867 DOI: 10.1055/a-2096-2960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/08/2023] [Indexed: 09/21/2023] Open
Abstract
Background and study aims Artificial intelligence (AI) in gastrointestinal endoscopy is developing very fast. Computer-aided detection of polyps and computer-aided diagnosis (CADx) for polyp characterization are available now. This study was performed to evaluate the diagnostic performance of a new commercially available CADx system in clinical practice. Patients and methods This prospective, non-randomized study was performed at a tertiary academic endoscopy center from March to August 2022. We included patients receiving a colonoscopy. Polypectomy had to be performed in all polyps. Every patient was examined concurrently by an endoscopist and AI using two opposing screens. The AI system, overseen by a second observer, was not visible to the endoscopist. The primary outcome was accuracy of the AI classifying the polyps into "neoplastic" and "non-neoplastic." The secondary outcome was accuracy of the classification by the endoscopists. Sessile serrated lesions were classified as neoplastic. Results We included 156 patients (mean age 65; 57 women) with 262 polyps ≤10 mm. Eighty-four were hyperplastic polyps (32.1%), 158 adenomas (60.3%), seven sessile serrated lesions (2.7%) and 13 other entities (normal/inflammatory colonmucosa, lymphoidic polyp) (4.9%) on histological diagnosis. Sensitivity, specificity and accuracy of AI were 89.70% (95% confidence interval [CI]: 84.02%-93.88%), 75.26% (95% CI: 65.46%-83.46%) and 84.35% (95% CI:79.38%-88.53%), respectively. Sensitivity, specificity and accuracy for less experienced endoscopists (2-5 years of endoscopy) were 95.56% (95% CI: 84.85%-99.46%), 61.54% (95% CI: 40.57%-79.77%) and 83.10% (95% CI: 72.34%-90.95%) and for experienced endoscopists 90.83% (95% CI: 84.19%-95.33%), 71.83% (95% CI: 59.90%-81.87%) and 83.77% (95% CI: 77.76%-88.70%), respectively. Conclusion Accuracy for polyp characterization by a new commercially available AI system is high, but does not fulfill the criteria for a "resect-and-discard" strategy.
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Affiliation(s)
- Sebastian Baumer
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Kilian Streicher
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Saleh A. Alqahtani
- Department of Gastroenterology and Hepatology, Johns Hopkins Hospital, Baltimore, United States
- Liver Transplant Center, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Dominic Brookman-Amissah
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Monika Brunner
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Christoph Federle
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Klaus Muehlenberg
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Lukas Pfeifer
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Andrea Salzberger
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Wolfgang Schorr
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Jozef Zustin
- Private Practice, Histopathology Service Private Practice, Regensburg, Germany
- Gerhard-Domagk-Institute of Pathology, Universitätsklinikum Münster, Munster, Germany
| | - Oliver Pech
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
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Li JW, Wu CCH, Lee JWJ, Liang R, Soon GST, Wang LM, Koh XH, Koh CJ, Chew WD, Lin KW, Thian MY, Matthew R, Kim G, Khor CJL, Fock KM, Ang TL, So JBY. Real-World Validation of a Computer-Aided Diagnosis System for Prediction of Polyp Histology in Colonoscopy: A Prospective Multicenter Study. Am J Gastroenterol 2023; 118:1353-1364. [PMID: 37040553 DOI: 10.14309/ajg.0000000000002282] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 03/28/2023] [Indexed: 04/13/2023]
Abstract
INTRODUCTION Computer-aided diagnosis (CADx) of polyp histology could support endoscopists in clinical decision-making. However, this has not been validated in a real-world setting. METHODS We performed a prospective, multicenter study comparing CADx and endoscopist predictions of polyp histology in real-time colonoscopy. Optical diagnosis based on visual inspection of polyps was made by experienced endoscopists. After this, the automated output from the CADx support tool was recorded. All imaged polyps were resected for histological assessment. Primary outcome was difference in diagnostic performance between CADx and endoscopist prediction of polyp histology. Subgroup analysis was performed for polyp size, bowel preparation, difficulty of location of the polyps, and endoscopist experience. RESULTS A total of 661 eligible polyps were resected in 320 patients aged ≥40 years between March 2021 and July 2022. CADx had an overall accuracy of 71.6% (95% confidence interval [CI] 68.0-75.0), compared with 75.2% (95% CI 71.7-78.4) for endoscopists ( P = 0.023). The sensitivity of CADx for neoplastic polyps was 61.8% (95% CI 56.9-66.5), compared with 70.3% (95% CI 65.7-74.7) for endoscopists ( P < 0.001). The interobserver agreement between CADx and endoscopist predictions of polyp histology was moderate (83.1% agreement, κ 0.661). When there was concordance between CADx and endoscopist predictions, the accuracy increased to 78.1%. DISCUSSION The overall diagnostic accuracy and sensitivity for neoplastic polyps was higher in experienced endoscopists compared with CADx predictions, with moderate interobserver agreement. Concordance in predictions increased this diagnostic accuracy. Further research is required to improve the performance of CADx and to establish its role in clinical practice.
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Affiliation(s)
- James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore
- Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore
| | - Clement Chun Ho Wu
- Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore
- Department of Gastroenterology and Hepatology, Singapore General Hospital, Singapore Health Services, Singapore
| | - Jonathan Wei Jie Lee
- Division of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, National University Health System, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Institute of Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore
| | - Raymond Liang
- Department of Gastroenterology and Hepatology, Tan Tock Seng Hospital, National Healthcare Group, Singapore
| | - Gwyneth Shook Ting Soon
- Department of Pathology, National University Hospital, National University Health System, Singapore
| | - Lai Mun Wang
- Department of Laboratory Medicine, Changi General Hospital, Singapore Health Services, Singapore
| | - Xuan Han Koh
- Department of Health Sciences Research, Changi General Hospital, Singapore
| | - Calvin Jianyi Koh
- Division of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, National University Health System, Singapore
| | - Wei Da Chew
- Department of Gastroenterology and Hepatology, Tan Tock Seng Hospital, National Healthcare Group, Singapore
| | - Kenneth Weicong Lin
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore
- Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore
| | - Mann Yie Thian
- Department of Gastroenterology and Hepatology, Tan Tock Seng Hospital, National Healthcare Group, Singapore
| | - Ronnie Matthew
- Department of Colorectal Surgery, Singapore General Hospital, Singapore Health Services, Singapore
| | - Guowei Kim
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- University Surgical Cluster, National University Hospital, Singapore
| | - Christopher Jen Lock Khor
- Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore
- Department of Gastroenterology and Hepatology, Singapore General Hospital, Singapore Health Services, Singapore
| | - Kwong Ming Fock
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore
- Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore
- Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore
| | - Jimmy Bok Yan So
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- University Surgical Cluster, National University Hospital, Singapore
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van Bokhorst QNE, Houwen BBSL, Hazewinkel Y, Fockens P, Dekker E. Advances in artificial intelligence and computer science for computer-aided diagnosis of colorectal polyps: current status. Endosc Int Open 2023; 11:E752-E767. [PMID: 37593158 PMCID: PMC10431975 DOI: 10.1055/a-2098-1999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 05/08/2023] [Indexed: 08/19/2023] Open
Affiliation(s)
- Querijn N E van Bokhorst
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, location Academic Medical Center, Amsterdam, the Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, the Netherlands
| | - Britt B S L Houwen
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, location Academic Medical Center, Amsterdam, the Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, the Netherlands
| | - Yark Hazewinkel
- Department of Gastroenterology and Hepatology, Tergooi Medical Center, Hilversum, the Netherlands
| | - Paul Fockens
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, location Academic Medical Center, Amsterdam, the Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, the Netherlands
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, location Academic Medical Center, Amsterdam, the Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, the Netherlands
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Houwen BBSL, Hazewinkel Y, Giotis I, Vleugels JLA, Mostafavi NS, van Putten P, Fockens P, Dekker E. Computer-aided diagnosis for optical diagnosis of diminutive colorectal polyps including sessile serrated lesions: a real-time comparison with screening endoscopists. Endoscopy 2023; 55:756-765. [PMID: 36623839 PMCID: PMC10374350 DOI: 10.1055/a-2009-3990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 01/09/2023] [Indexed: 01/11/2023]
Abstract
BACKGROUND : We aimed to compare the accuracy of the optical diagnosis of diminutive colorectal polyps, including sessile serrated lesions (SSLs), between a computer-aided diagnosis (CADx) system and endoscopists during real-time colonoscopy. METHODS : We developed the POLyp Artificial Recognition (POLAR) system, which was capable of performing real-time characterization of diminutive colorectal polyps. For pretraining, the Microsoft-COCO dataset with over 300 000 nonpolyp object images was used. For training, eight hospitals prospectively collected 2637 annotated images from 1339 polyps (i. e. publicly available online POLAR database). For clinical validation, POLAR was tested during colonoscopy in patients with a positive fecal immunochemical test (FIT), and compared with the performance of 20 endoscopists from eight hospitals. Endoscopists were blinded to the POLAR output. Primary outcome was the comparison of accuracy of the optical diagnosis of diminutive colorectal polyps between POLAR and endoscopists (neoplastic [adenomas and SSLs] versus non-neoplastic [hyperplastic polyps]). Histopathology served as the reference standard. RESULTS : During clinical validation, 423 diminutive polyps detected in 194 FIT-positive individuals were included for analysis (300 adenomas, 41 SSLs, 82 hyperplastic polyps). POLAR distinguished neoplastic from non-neoplastic lesions with 79 % accuracy, 89 % sensitivity, and 38 % specificity. The endoscopists achieved 83 % accuracy, 92 % sensitivity, and 44 % specificity. The optical diagnosis accuracy between POLAR and endoscopists was not significantly different (P = 0.10). The proportion of polyps in which POLAR was able to provide an optical diagnosis was 98 % (i. e. success rate). CONCLUSIONS : We developed a CADx system that differentiated neoplastic from non-neoplastic diminutive polyps during endoscopy, with an accuracy comparable to that of screening endoscopists and near-perfect success rate.
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Affiliation(s)
- Britt B. S. L. Houwen
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Yark Hazewinkel
- Department of Gastroenterology and Hepatology, Radboud University Nijmegen Medical Center, Radboud University of Nijmegen, Nijmegen, The Netherlands
| | | | - Jasper L. A. Vleugels
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Nahid S. Mostafavi
- Department of Gastroenterology and Hepatology, Subdivision Statistics, Amsterdam University Medical Center, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Paul van Putten
- Department of Gastroenterology and Hepatology, Medical Center Leeuwarden, Leeuwarden, The Netherlands
| | - Paul Fockens
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, the Netherlands
- Bergman Clinics Maag and Darm Amsterdam, Amsterdam, The Netherlands
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Hassan C, Repici A, Sharma P. Incorporating Artificial Intelligence Into Gastroenterology Practices. Clin Gastroenterol Hepatol 2023; 21:1687-1689. [PMID: 36841445 DOI: 10.1016/j.cgh.2023.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
Affiliation(s)
- Cesare Hassan
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center - IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Alessandro Repici
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center - IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Prateek Sharma
- Division of Gastroenterology and Hepatology, VA Medical Center and University of Kansas School of Medicine, Kansas City, Kansas.
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Maida M, Marasco G, Facciorusso A, Shahini E, Sinagra E, Pallio S, Ramai D, Murino A. Effectiveness and application of artificial intelligence for endoscopic screening of colorectal cancer: the future is now. Expert Rev Anticancer Ther 2023; 23:719-729. [PMID: 37194308 DOI: 10.1080/14737140.2023.2215436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 05/15/2023] [Indexed: 05/18/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) in gastrointestinal endoscopy includes systems designed to interpret medical images and increase sensitivity during examination. This may be a promising solution to human biases and may provide support during diagnostic endoscopy. AREAS COVERED This review aims to summarize and evaluate data supporting AI technologies in lower endoscopy, addressing their effectiveness, limitations, and future perspectives. EXPERT OPINION Computer-aided detection (CADe) systems have been studied with promising results, allowing for an increase in adenoma detection rate (ADR), adenoma per colonoscopy (APC), and a reduction in adenoma miss rate (AMR). This may lead to an increase in the sensitivity of endoscopic examinations and a reduction in the risk of interval-colorectal cancer. In addition, computer-aided characterization (CADx) has also been implemented, aiming to distinguish adenomatous and non-adenomatous lesions through real-time assessment using advanced endoscopic imaging techniques. Moreover, computer-aided quality (CADq) systems have been developed with the aim of standardizing quality measures in colonoscopy (e.g. withdrawal time and adequacy of bowel cleansing) both to improve the quality of examinations and set a reference standard for randomized controlled trials.
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Affiliation(s)
- Marcello Maida
- Gastroenterology and Endoscopy Unit, S. Elia-Raimondi Hospital, Caltanissetta, Italy
| | - Giovanni Marasco
- IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Antonio Facciorusso
- Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
| | - Endrit Shahini
- Gastroenterology Unit, National Institute of Gastroenterology-IRCCS "Saverio de Bellis", Castellana Grotte, Bari, Italy
| | - Emanuele Sinagra
- Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, Cefalu, Italy
| | - Socrate Pallio
- Digestive Diseases Endoscopy Unit, Policlinico G. Martino Hospital, University of Messina, Messina, Italy
| | - Daryl Ramai
- Gastroenterology & Hepatology, University of Utah Health, Salt Lake City, UT, USA
| | - Alberto Murino
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, UK
- Department of Gastroenterology, Cleveland Clinic London, London, UK
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Galati JS, Lin K, Gross SA. Recent advances in devices and technologies that might prove revolutionary for colonoscopy procedures. Expert Rev Med Devices 2023; 20:1087-1103. [PMID: 37934873 DOI: 10.1080/17434440.2023.2280773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 11/03/2023] [Indexed: 11/09/2023]
Abstract
INTRODUCTION Colorectal cancer (CRC) is the third most common malignancy and second leading cause of cancer-related mortality in the world. Adenoma detection rate (ADR), a quality indicator for colonoscopy, has gained prominence as it is inversely related to CRC incidence and mortality. As such, recent efforts have focused on developing novel colonoscopy devices and technologies to improve ADR. AREAS COVERED The main objective of this paper is to provide an overview of advancements in the fields of colonoscopy mechanical attachments, artificial intelligence-assisted colonoscopy, and colonoscopy optical enhancements with respect to ADR. We accomplished this by performing a comprehensive search of multiple electronic databases from inception to September 2023. This review is intended to be an introduction to colonoscopy devices and technologies. EXPERT OPINION Numerous mechanical attachments and optical enhancements have been developed that have the potential to improve ADR and AI has gone from being an inaccessible concept to a feasible means for improving ADR. While these advances are exciting and portend a change in what will be considered standard colonoscopy, they continue to require refinement. Future studies should focus on combining modalities to further improve ADR and exploring the use of these technologies in other facets of colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Internal Medicine, NYU Langone Health, New York, NY, USA
| | - Kevin Lin
- Department of Internal Medicine, NYU Langone Health, New York, NY, USA
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY, USA
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Mansour NM. Artificial Intelligence in Colonoscopy. Curr Gastroenterol Rep 2023; 25:122-129. [PMID: 37129831 DOI: 10.1007/s11894-023-00872-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/03/2023] [Indexed: 05/03/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is a rapidly growing field in gastrointestinal endoscopy, and its potential applications are virtually endless, with studies demonstrating use of AI for early gastric cancer, inflammatory bowel disease, Barrett's esophagus, capsule endoscopy, as well as other areas in gastroenterology. Much of the early studies and applications of AI in gastroenterology have revolved around colonoscopy, particularly with regards to real-time polyp detection and characterization. This review will cover much of the existing data on computer-aided detection (CADe), computer-aided diagnosis (CADx), and briefly discuss some other interesting applications of AI for colonoscopy, while also considering some of the challenges and limitations that exist around the use of AI for colonoscopy. RECENT FINDINGS Multiple randomized controlled trials have now been published which show a statistically significant improvement when using AI to improve adenoma detection and reduce adenoma miss rates during colonoscopy. There is also a growing pool of literature showing that AI can be helpful for characterizing/diagnosing colorectal polyps in real time. AI has also shown promise in other areas of colonoscopy, including polyp sizing and automated measurement and monitoring of quality metrics during colonoscopy. AI is a promising tool that has the ability to shape the future of gastrointestinal endoscopy, with much of the early data showing significant benefits to use of AI during colonoscopy. However, there remain several challenges that may delay or hamper the widespread use of AI in the field.
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Affiliation(s)
- Nabil M Mansour
- Section of Gastroenterology and Hepatology, Baylor College of Medicine, 7200 Cambridge St., Suite 8B, Houston, TX, 77030, USA.
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Gimeno-García AZ, Hernández-Pérez A, Nicolás-Pérez D, Hernández-Guerra M. Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward? Cancers (Basel) 2023; 15:cancers15082193. [PMID: 37190122 DOI: 10.3390/cancers15082193] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/04/2023] [Accepted: 04/05/2023] [Indexed: 05/17/2023] Open
Abstract
Growing evidence indicates that artificial intelligence (AI) applied to medicine is here to stay. In gastroenterology, AI computer vision applications have been stated as a research priority. The two main AI system categories are computer-aided polyp detection (CADe) and computer-assisted diagnosis (CADx). However, other fields of expansion are those related to colonoscopy quality, such as methods to objectively assess colon cleansing during the colonoscopy, as well as devices to automatically predict and improve bowel cleansing before the examination, predict deep submucosal invasion, obtain a reliable measurement of colorectal polyps and accurately locate colorectal lesions in the colon. Although growing evidence indicates that AI systems could improve some of these quality metrics, there are concerns regarding cost-effectiveness, and large and multicentric randomized studies with strong outcomes, such as post-colonoscopy colorectal cancer incidence and mortality, are lacking. The integration of all these tasks into one quality-improvement device could facilitate the incorporation of AI systems in clinical practice. In this manuscript, the current status of the role of AI in colonoscopy is reviewed, as well as its current applications, drawbacks and areas for improvement.
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Affiliation(s)
- Antonio Z Gimeno-García
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - Anjara Hernández-Pérez
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - David Nicolás-Pérez
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - Manuel Hernández-Guerra
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
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Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening. Diagnostics (Basel) 2023; 13:diagnostics13061102. [PMID: 36980409 PMCID: PMC10047293 DOI: 10.3390/diagnostics13061102] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/19/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023] Open
Abstract
Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic screening program is expected to strongly decrease colorectal cancer incidence and mortality. In this regard, quality of colonoscopy has been clearly related with the risk of post-colonoscopy colorectal cancer. Recently, the development of artificial intelligence (AI) applications in the medical field has been growing in interest. Through machine learning processes, and, more recently, deep learning, if a very high numbers of learning samples are available, AI systems may automatically extract specific features from endoscopic images/videos without human intervention, helping the endoscopists in different aspects of their daily practice. The aim of this review is to summarize the current knowledge on AI-aided endoscopy, and to outline its potential role in colorectal cancer prevention.
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