Reference Citation Analysis: Find an Article, Find a Category, Find a Journal, Find a Scholar

For: Tontini GE, Rimondi A, Vernero M, Neumann H, Vecchi M, Bezzio C, Cavallaro F. Artificial intelligence in gastrointestinal endoscopy for inflammatory bowel disease: a systematic review and new horizons. Therap Adv Gastroenterol 2021;14:17562848211017730. [PMID: 34178115 PMCID: PMC8202249 DOI: 10.1177/17562848211017730] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 04/26/2021] [Indexed: 02/04/2023] Open

For:	Tontini GE, Rimondi A, Vernero M, Neumann H, Vecchi M, Bezzio C, Cavallaro F. Artificial intelligence in gastrointestinal endoscopy for inflammatory bowel disease: a systematic review and new horizons. Therap Adv Gastroenterol 2021;14:17562848211017730. [PMID: 34178115 PMCID: PMC8202249 DOI: 10.1177/17562848211017730] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 04/26/2021] [Indexed: 02/04/2023] Open

Number

Cited by Other Article(s)

Guo YX, Yan X, Liu XC, Liu YX, Liu C. Artificial intelligence-driven strategies for managing renal and urinary complications in inflammatory bowel disease. World J Nephrol 2025;14:100825. [DOI: 10.5527/wjn.v14.i1.100825] [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: 08/27/2024] [Revised: 11/29/2024] [Accepted: 12/27/2024] [Indexed: 01/20/2025] Open

Gutierrez-Becker B, Fraessle S, Yao H, Luscher J, Girycki R, Machura B, Czornik J, Goslinsky J, Pitura M, Levitte S, Arús-Pous J, Fisher E, Bojic D, Richmond D, Bigorgne AE, Prunotto M. Ulcerative Colitis Severity Classification and Localized Extent (UC-SCALE): An Artificial Intelligence Scoring System for a Spatial Assessment of Disease Severity in Ulcerative Colitis. J Crohns Colitis 2025;19:jjae187. [PMID: 39657580 DOI: 10.1093/ecco-jcc/jjae187] [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: 08/30/2024] [Revised: 11/04/2024] [Accepted: 12/06/2024] [Indexed: 12/12/2024]

Abstract

BACKGROUND AND AIMS

Validated scoring methods such as the Mayo Clinic Endoscopic Subscore (MCES) evaluate ulcerative colitis (UC) severity at the worst colon segment, without considering disease extent. We present the Ulcerative Colitis Severity Classification and Localized Extent (UC-SCALE) algorithm, which provides a comprehensive and automated evaluation of endoscopic severity and disease extent in UC.

METHODS

Ulcerative Colitis Severity Classification and Localized Extent consists of 3 main elements: (1) a quality filter selecting readable images (frames) from colonoscopy videos, (2) a scoring system assigning an MCES to each readable frame, and (3) a camera localization algorithm assigning each frame to a location within the colon. Ulcerative Colitis Severity Classification and Localized Extent was trained and tested using 4326 sigmoidoscopy videos from phase III Etrolizumab clinical trials.

RESULTS

The high agreement between UC-SCALE and central reading at the level of the colon section (𝜅 = 0.80), and the agreement between central and local reading (𝜅 = 0.84), suggested a similar inter-rater agreement between UC-SCALE and experienced readers. Furthermore, UC-SCALE correlated with disease activity markers such calprotectin, C-reactive protein and patient-reported outcomes, Physician Global Assessment and Geboes Histologic scores (rs 0.40-0.55, ps < 0.0001). Finally, the value of using UC-SCALE was demonstrated by assessing individual endoscopic severity between baseline and induction.

CONCLUSIONS

Our fully automated scoring system enables accurate, objective, and localized assessment of endoscopic severity in UC patients. In addition, we provide a topological representation of the score as a marker of disease severity that correlates highly with clinical metrics. Ulcerative Colitis Severity Classification and Localized Extent reproduces central reading and holds promise to enhance disease severity evaluation in both clinical trials and everyday practice.

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Eidler P, Kopylov U, Ukashi O. Capsule Endoscopy in Inflammatory Bowel Disease: Evolving Role and Recent Advances. Gastrointest Endosc Clin N Am 2025;35:73-102. [PMID: 39510694 DOI: 10.1016/j.giec.2024.07.002] [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: 11/15/2024]

Fasulo E, D’Amico F, Zilli A, Furfaro F, Cicerone C, Parigi TL, Peyrin-Biroulet L, Danese S, Allocca M. Advancing Colorectal Cancer Prevention in Inflammatory Bowel Disease (IBD): Challenges and Innovations in Endoscopic Surveillance. Cancers (Basel) 2024;17:60. [PMID: 39796690 PMCID: PMC11718813 DOI: 10.3390/cancers17010060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2024] [Revised: 12/27/2024] [Accepted: 12/27/2024] [Indexed: 01/13/2025] Open

Affiliation(s)

Ernesto Fasulo Department of Gastroenterology and Endoscopy, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, 20132 Milan, Italy; (E.F.); (F.D.); (A.Z.); (F.F.); (C.C.); (T.L.P.); (S.D.)
Ferdinando D’Amico Department of Gastroenterology and Endoscopy, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, 20132 Milan, Italy; (E.F.); (F.D.); (A.Z.); (F.F.); (C.C.); (T.L.P.); (S.D.)
Alessandra Zilli Department of Gastroenterology and Endoscopy, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, 20132 Milan, Italy; (E.F.); (F.D.); (A.Z.); (F.F.); (C.C.); (T.L.P.); (S.D.)
Federica Furfaro Department of Gastroenterology and Endoscopy, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, 20132 Milan, Italy; (E.F.); (F.D.); (A.Z.); (F.F.); (C.C.); (T.L.P.); (S.D.)
Clelia Cicerone Department of Gastroenterology and Endoscopy, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, 20132 Milan, Italy; (E.F.); (F.D.); (A.Z.); (F.F.); (C.C.); (T.L.P.); (S.D.)
Tommaso Lorenzo Parigi Department of Gastroenterology and Endoscopy, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, 20132 Milan, Italy; (E.F.); (F.D.); (A.Z.); (F.F.); (C.C.); (T.L.P.); (S.D.)
Laurent Peyrin-Biroulet Department of Gastroenterology, Nancy University Hospital, F-54500 Vandœuvre-lès-Nancy, France; NSERM, NGERE, University of Lorraine, F-54000 Nancy, France INFINY Institute, Nancy University Hospital, F-54500 Vandœuvre-lès-Nancy, France FHU-CURE, Nancy University Hospital, F-54500 Vandœuvre-lès-Nancy, France Groupe Hospitalier Privé Ambroise Paré-Hartmann, Paris IBD Center, F-92200 Neuilly sur Seine, France Division of Gastroenterology and Hepatology, McGill University Health Centre, Montreal, QC H4A 3J1, Canada
Silvio Danese Department of Gastroenterology and Endoscopy, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, 20132 Milan, Italy; (E.F.); (F.D.); (A.Z.); (F.F.); (C.C.); (T.L.P.); (S.D.)
Mariangela Allocca Department of Gastroenterology and Endoscopy, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, 20132 Milan, Italy; (E.F.); (F.D.); (A.Z.); (F.F.); (C.C.); (T.L.P.); (S.D.)

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Akbarian P, Asadi F, Sabahi A. Developing Mobile Health Applications for Inflammatory Bowel Disease: A Systematic Review of Features and Technologies. Middle East J Dig Dis 2024;16:211-220. [PMID: 39807416 PMCID: PMC11725021 DOI: 10.34172/mejdd.2024.394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Accepted: 09/27/2024] [Indexed: 01/16/2025] Open

Abstract

Background

Patients with inflammatory bowel disease (IBD) require lifelong treatment, which significantly impacts their quality of life. Self-management of this disease is an effective factor in managing chronic conditions and improving patients' quality of life. The use of mobile applications is a novel approach to providing self-management models and healthcare services for patients with IBD. The present systematic review aimed to identify the features and technologies used in the development of IBD disease management applications.

Methods

This systematic review was conducted according to PRISMA guidelines in PubMed, Scopus, and Web of Sciences databases up to August 8, 2023, which included initial searches, screening studies, assessing eligibility and risk of bias, and study selection. The data extraction form was based on the study objectives, including bibliographic information from articles, such as the first author's name, year of publication, country of origin, and details related to mobile health applications, such as the name of the application, features and technologies used, advantages and disadvantages, main outcomes, and other results. The content of the research was analyzed according to the research objectives.

Results

In the initial review of four databases, a total of 160 articles were retrieved and subsequently entered into EndNote. After removing duplicates and irrelevant studies based on title, abstract, and full-text assessments, 12 articles were finally selected. The studies were conducted between the years 2015 and 2024. 100% of the applications designed for patients with IBD were aimed at treatment, 83% were for self-management of the disease, and 33% of the applications were intended for disease diagnosis. The features of IBD management applications were categorized into four groups: education, monitoring, counseling, and diagnosis and treatment.

Conclusion

Various mobile applications have been developed for the management of IBD, each differing in features and technologies used. While current IBD applications have limited capabilities in diagnosing disease severity, they still hold significant potential in empowering patients through education, counseling, and monitoring. The integration of artificial intelligence and decision support systems may enhance the effectiveness and reliability of these applications.

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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

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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Naqvi HA, Delungahawatta T, Atarere JO, Bandaru SK, Barrow JB, Mattar MC. Evaluation of online chat-based artificial intelligence responses about inflammatory bowel disease and diet. Eur J Gastroenterol Hepatol 2024;36:1109-1112. [PMID: 38973528 DOI: 10.1097/meg.0000000000002815] [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: 07/09/2024]

Chen YF, Liu L, Lyu B, Yang Y, Zheng SS, Huang X, Xu Y, Fan YH. Role of artificial intelligence in Crohn's disease intestinal strictures and fibrosis. J Dig Dis 2024;25:476-483. [PMID: 39191433 DOI: 10.1111/1751-2980.13308] [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: 12/27/2023] [Revised: 07/21/2024] [Accepted: 08/07/2024] [Indexed: 08/29/2024]

Rimondi A, Gottlieb K, Despott EJ, Iacucci M, Murino A, Tontini GE. Can artificial intelligence replace endoscopists when assessing mucosal healing in ulcerative colitis? A systematic review and diagnostic test accuracy meta-analysis. Dig Liver Dis 2024;56:1164-1172. [PMID: 38057218 DOI: 10.1016/j.dld.2023.11.005] [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: 09/04/2023] [Revised: 11/03/2023] [Accepted: 11/07/2023] [Indexed: 12/08/2023]

Dương TQ, Soldera J. Virtual reality tools for training in gastrointestinal endoscopy: A systematic review. Artif Intell Gastrointest Endosc 2024;5:92090. [DOI: 10.37126/aige.v5.i2.92090] [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: 01/14/2024] [Revised: 02/11/2024] [Accepted: 04/07/2024] [Indexed: 05/11/2024] Open

Abstract

BACKGROUND

Virtual reality (VR) has emerged as an innovative technology in endoscopy training, providing a simulated environment that closely resembles real-life scenarios and offering trainees a valuable platform to acquire and enhance their endoscopic skills. This systematic review will critically evaluate the effectiveness and feasibility of VR-based training compared to traditional methods.

AIM

To evaluate the effectiveness and feasibility of VR-based training compared to traditional methods. By examining the current state of the field, this review seeks to identify gaps, challenges, and opportunities for further research and implemen-tation of VR in endoscopic training.

METHODS

The study is a systematic review, following the guidelines for reporting systematic reviews set out by the PRISMA statement. A comprehensive search command was designed and implemented and run in September 2023 to identify relevant studies available, from electronic databases such as PubMed, Scopus, Cochrane, and Google Scholar. The results were systematically reviewed.

RESULTS

Sixteen articles were included in the final analysis. The total number of participants was 523. Five studies focused on both upper endoscopy and colonoscopy training, two on upper endoscopy training only, eight on colon-oscopy training only, and one on sigmoidoscopy training only. Gastro-intestinal Mentor virtual endoscopy simulator was commonly used. Fifteen reported positive results, indicating that VR-based training was feasible and acceptable for endoscopy learners. VR technology helped the trainees enhance their skills in manipulating the endoscope, reducing the procedure time or increasing the technical accuracy, in VR scenarios and real patients. Some studies show that the patient discomfort level decreased significantly. However, some studies show there were no significant differences in patient discomfort and pain scores between VR group and other groups.

CONCLUSION

VR training is effective for endoscopy training. There are several well-designed randomized controlled trials with large sample sizes, proving the potential of this innovative tool. Thus, VR should be more widely adopted in endoscopy training. Furthermore, combining VR training with conventional methods could be a promising approach that should be implemented in training.

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Guo F, Meng H. Application of artificial intelligence in gastrointestinal endoscopy. Arab J Gastroenterol 2024;25:93-96. [PMID: 38228443 DOI: 10.1016/j.ajg.2023.12.010] [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: 02/24/2023] [Revised: 09/06/2023] [Accepted: 12/30/2023] [Indexed: 01/18/2024]

Ali H, Muzammil MA, Dahiya DS, Ali F, Yasin S, Hanif W, Gangwani MK, Aziz M, Khalaf M, Basuli D, Al-Haddad M. Artificial intelligence in gastrointestinal endoscopy: a comprehensive review. Ann Gastroenterol 2024;37:133-141. [PMID: 38481787 PMCID: PMC10927620 DOI: 10.20524/aog.2024.0861] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 12/05/2023] [Indexed: 02/14/2025] Open

Santacroce G, Zammarchi I, Tan CK, Coppola G, Varley R, Ghosh S, Iacucci M. Present and future of endoscopy precision for inflammatory bowel disease. Dig Endosc 2024;36:292-304. [PMID: 37643635 DOI: 10.1111/den.14672] [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: 06/12/2023] [Accepted: 08/28/2023] [Indexed: 08/31/2023]

Pal P, Pooja K, Nabi Z, Gupta R, Tandan M, Rao GV, Reddy N. Artificial intelligence in endoscopy related to inflammatory bowel disease: A systematic review. Indian J Gastroenterol 2024;43:172-187. [PMID: 38418774 DOI: 10.1007/s12664-024-01531-3] [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: 11/09/2023] [Accepted: 01/08/2024] [Indexed: 03/02/2024]

Abstract

BACKGROUND AND OBJECTIVES

In spite of rapid growth of artificial intelligence (AI) in digestive endoscopy in lesion detection and characterization, the role of AI in inflammatory bowel disease (IBD) endoscopy is not clearly defined. We aimed at systematically reviewing the role of AI in IBD endoscopy and identifying future research areas.

METHODS

We searched the PubMed and Embase database using keywords ("artificial intelligence" OR "machine learning" OR "computer-aided" OR "convolutional neural network") AND ("inflammatory bowel disease" OR "ulcerative colitis" OR "Crohn's") AND ("endoscopy" or "colonoscopy" or "capsule endoscopy" or "device assisted enteroscopy") between 1975 and September 2023 and identified 62 original articles for detailed review. Review articles, consensus guidelines, case reports/series, editorials, letter to the editor, non-peer-reviewed pre-prints and conference abstracts were excluded. The quality of the included studies was assessed using the MI-CLAIM checklist.

RESULTS

The accuracy of AI models (25 studies) to assess ulcerative colitis (UC) endoscopic activity ranged between 86.54% and 94.5%. AI-assisted capsule endoscopy reading (12 studies) substantially reduced analyzable images and reading time with excellent accuracy (90.5% to 99.9%). AI-assisted analysis of colonoscopic images can help differentiate IBD from non-IBD, UC from non-UC and UC from Crohn's disease (CD) (three studies) with 72.1%, 98.3% and > 90% accuracy, respectively. AI models based on non-invasive clinical and radiologic parameters could predict endoscopic activity (three studies). AI-assisted virtual chromoendoscopy (four studies) could predict histologic remission and long-term outcomes. Computer-assisted detection (CADe) of dysplasia (two studies) is feasible along with AI-based differentiation of high from low-grade IBD neoplasia (79% accuracy). AI is effective in linking electronic medical record data (two studies) with colonoscopic videos to facilitate widespread machine learning.

CONCLUSION

AI-assisted IBD endoscopy has the potential to impact clinical management by automated detection and characterization of endoscopic lesions. Large, multi-center, prospective studies and commercially available IBD-specific endoscopic AI algorithms are warranted.

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Reinisch W, Pradhan V, Ahmad S, Zhang Z, Gale JD. Alternative Endoscopy Reading Paradigms Determine Score Reliability and Effect Size in Ulcerative Colitis. J Crohns Colitis 2024;18:82-90. [PMID: 37616127 PMCID: PMC10821708 DOI: 10.1093/ecco-jcc/jjad134] [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: 03/24/2023] [Indexed: 08/25/2023]

Chen C, Tang F, Herth FJF, Zuo Y, Ren J, Zhang S, Jian W, Tang C, Li S. Building and validating an artificial intelligence model to identify tracheobronchopathia osteochondroplastica by using bronchoscopic images. Ther Adv Respir Dis 2024;18:17534666241253694. [PMID: 38803144 PMCID: PMC11131396 DOI: 10.1177/17534666241253694] [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: 06/14/2023] [Accepted: 04/22/2024] [Indexed: 05/29/2024] Open

Abstract

BACKGROUND

Given the rarity of tracheobronchopathia osteochondroplastica (TO), many young doctors in primary hospitals are unable to identify TO based on bronchoscopy findings.

OBJECTIVES

To build an artificial intelligence (AI) model for differentiating TO from other multinodular airway diseases by using bronchoscopic images.

DESIGN

We designed the study by comparing the imaging data of patients undergoing bronchoscopy from January 2010 to October 2022 by using EfficientNet. Bronchoscopic images of 21 patients with TO at Anhui Chest Hospital from October 2019 to October 2022 were collected for external validation.

METHODS

Bronchoscopic images of patients with multinodular airway lesions (including TO, amyloidosis, tumors, and inflammation) and without airway lesions in the First Affiliated Hospital of Guangzhou Medical University were collected. The images were randomized (4:1) into training and validation groups based on different diseases and utilized for deep learning by convolutional neural networks (CNNs).

RESULTS

We enrolled 201 patients with multinodular airway disease (38, 15, 75, and 73 patients with TO, amyloidosis, tumors, and inflammation, respectively) and 213 without any airway lesions. To find multinodular lesion images for deep learning, we utilized 2183 bronchoscopic images of multinodular lesions (including TO, amyloidosis, tumor, and inflammation) and compared them with images without any airway lesions (1733). The accuracy of multinodular lesion identification was 98.9%. Further, the accuracy of TO detection based on the bronchoscopic images of multinodular lesions was 89.2%. Regarding external validation (using images from 21 patients with TO), all patients could be diagnosed with TO; the accuracy was 89.8%.

CONCLUSION

We built an AI model that could differentiate TO from other multinodular airway diseases (mainly amyloidosis, tumors, and inflammation) by using bronchoscopic images. The model could help young physicians identify this rare airway disease.

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Wang M, Shi J, Yu C, Zhang X, Xu G, Xu Z, Ma Y. Emerging strategy towards mucosal healing in inflammatory bowel disease: what the future holds? Front Immunol 2023;14:1298186. [PMID: 38155971 PMCID: PMC10752988 DOI: 10.3389/fimmu.2023.1298186] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 11/30/2023] [Indexed: 12/30/2023] Open

Mascarenhas M, Ribeiro T, Afonso J, Mendes F, Cardoso P, Martins M, Ferreira J, Macedo G. Smart Endoscopy Is Greener Endoscopy: Leveraging Artificial Intelligence and Blockchain Technologies to Drive Sustainability in Digestive Health Care. Diagnostics (Basel) 2023;13:3625. [PMID: 38132209 PMCID: PMC10743290 DOI: 10.3390/diagnostics13243625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/14/2023] [Accepted: 11/25/2023] [Indexed: 12/23/2023] Open

Datres M, Paolazzi E, Chierici M, Pozzi M, Colangelo A, Dorian Donzella M, Jurman G. Endoscopy-based IBD identification by a quantized deep learning pipeline. BioData Min 2023;16:33. [PMID: 38001537 PMCID: PMC10675910 DOI: 10.1186/s13040-023-00350-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 11/18/2023] [Indexed: 11/26/2023] Open

Abstract

BACKGROUND

Discrimination between patients affected by inflammatory bowel diseases and healthy controls on the basis of endoscopic imaging is an challenging problem for machine learning models. Such task is used here as the testbed for a novel deep learning classification pipeline, powered by a set of solutions enhancing characterising elements such as reproducibility, interpretability, reduced computational workload, bias-free modeling and careful image preprocessing.

RESULTS

First, an automatic preprocessing procedure is devised, aimed to remove artifacts from clinical data, feeding then the resulting images to an aggregated per-patient model to mimic the clinicians decision process. The predictions are based on multiple snapshots obtained through resampling, reducing the risk of misleading outcomes by removing the low confidence predictions. Each patient's outcome is explained by returning the images the prediction is based upon, supporting clinicians in verifying diagnoses without the need for evaluating the full set of endoscopic images. As a major theoretical contribution, quantization is employed to reduce the complexity and the computational cost of the model, allowing its deployment on small power devices with an almost negligible 3% performance degradation. Such quantization procedure holds relevance not only in the context of per-patient models but also for assessing its feasibility in providing real-time support to clinicians even in low-resources environments. The pipeline is demonstrated on a private dataset of endoscopic images of 758 IBD patients and 601 healthy controls, achieving Matthews Correlation Coefficient 0.9 as top performance on test set.

CONCLUSION

We highlighted how a comprehensive pre-processing pipeline plays a crucial role in identifying and removing artifacts from data, solving one of the principal challenges encountered when working with clinical data. Furthermore, we constructively showed how it is possible to emulate clinicians decision process and how it offers significant advantages, particularly in terms of explainability and trust within the healthcare context. Last but not least, we proved that quantization can be a useful tool to reduce the time and resources consumption with an acceptable degradation of the model performs. The quantization study proposed in this work points up the potential development of real-time quantized algorithms as valuable tools to support clinicians during endoscopy procedures.

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Lv B, Ma L, Shi Y, Tao T, Shi Y. A systematic review and meta-analysis of artificial intelligence-diagnosed endoscopic remission in ulcerative colitis. iScience 2023;26:108120. [PMID: 37867944 PMCID: PMC10585391 DOI: 10.1016/j.isci.2023.108120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/08/2023] [Accepted: 09/29/2023] [Indexed: 10/24/2023] Open

Chen C, Herth FJF, Zuo Y, Li H, Liang X, Chen Y, Ren J, Jian W, Zhong C, Li S. Distinguishing bronchoscopically observed anatomical positions of airway under by convolutional neural network. Ther Adv Chronic Dis 2023;14:20406223231181495. [PMID: 37637372 PMCID: PMC10457519 DOI: 10.1177/20406223231181495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 05/24/2023] [Indexed: 08/29/2023] Open

Abstract

Background

Artificial intelligence (AI) technology has been used for finding lesions via gastrointestinal endoscopy. However, there were few AI-associated studies that discuss bronchoscopy.

Objectives

To use convolutional neural network (CNN) to recognize the observed anatomical positions of the airway under bronchoscopy.

Design

We designed the study by comparing the imaging data of patients undergoing bronchoscopy from March 2022 to October 2022 by using EfficientNet (one of the CNNs) and U-Net.

Methods

Based on the inclusion and exclusion criteria, 1527 clear images of normal anatomical positions of the airways from 200 patients were used for training, and 475 clear images from 72 patients were utilized for validation. Further, 20 bronchoscopic videos of examination procedures in another 20 patients with normal airway structures were used to extract the bronchoscopic images of normal anatomical positions to evaluate the accuracy for the model. Finally, 21 respiratory doctors were enrolled for the test of recognizing corrected anatomical positions using the validating datasets.

Results

In all, 1527 bronchoscopic images of 200 patients with nine anatomical positions of the airway, including carina, right main bronchus, right upper lobe bronchus, right intermediate bronchus, right middle lobe bronchus, right lower lobe bronchus, left main bronchus, left upper lobe bronchus, and left lower lobe bronchus, were used for supervised machine learning and training, and 475 clear bronchoscopic images of 72 patients were used for validation. The mean accuracy of recognizing these 9 positions was 91% (carina: 98%, right main bronchus: 98%, right intermediate bronchus: 90%, right upper lobe bronchus: 91%, right middle lobe bronchus 92%, right lower lobe bronchus: 83%, left main bronchus: 89%, left upper bronchus: 91%, left lower bronchus: 76%). The area under the curves for these nine positions were >0.98. In addition, the accuracy of extracting the images via the video by the trained model was 94.7%. We also conducted a deep learning study to segment 10 segment bronchi in right lung, and 8 segment bronchi in Left lung. Because of the problem of radial depth, only segment bronchi distributions below right upper bronchus and right middle bronchus could be correctly recognized. The accuracy of recognizing was 84.33 ± 7.52% by doctors receiving interventional pulmonology education in our hospital over 6 months.

Conclusion

Our study proved that AI technology can be used to distinguish the normal anatomical positions of the airway, and the model we trained could extract the corrected images via the video to help standardize data collection and control quality.

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Cai W, Xu J, Chen Y, Wu X, Zeng Y, Yu F. Performance of Machine Learning Algorithms for Predicting Disease Activity in Inflammatory Bowel Disease. Inflammation 2023:10.1007/s10753-023-01827-0. [PMID: 37171693 DOI: 10.1007/s10753-023-01827-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 04/17/2023] [Accepted: 04/24/2023] [Indexed: 05/13/2023]

Abstract

This study aimed to explore the effectiveness of predicting disease activity in patients with inflammatory bowel disease (IBD), using machine learning (ML) models. A retrospective research was undertaken on IBD patients who were admitted into the First Affiliated Hospital of Wenzhou Medical University between September 2011 and September 2019. At first, data were randomly split into a 3:1 ratio of training to test set. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to reduce the dimension of variables. These variables were used to generate seven ML algorithms, namely random forests (RFs), adaptive boosting (AdaBoost), K-nearest neighbors (KNNs), support vector machines (SVMs), naïve Bayes (NB), ridge regression, and eXtreme gradient boosting (XGBoost) to train to predict disease activity in IBD patients. SHapley Additive exPlanation (SHAP) analysis was performed to rank variable importance. A total of 876 participants with IBD, consisting of 275 ulcerative colitis (UC) and 601 Crohn's disease (CD), were retrospectively enrolled in the study. Thirty-three variables were obtained from the clinical characteristics and laboratory tests of the participants. Finally, after LASSO analysis, 11 and 5 variables were screened out to construct ML models for CD and UC, respectively. All seven ML models performed well in predicting disease activity in the CD and UC test sets. Among these ML models, SVM was more effective in predicting disease activity in the CD group, whose AUC reached 0.975, sensitivity 0.947, specificity 0.920, and accuracy 0.933. AdaBoost performed best for the UC group, with an AUC of 0.911, sensitivity 0.844, specificity 0.875, and accuracy 0.855. ML algorithms were available and capable of predicting disease activity in IBD patients. Based on clinical and laboratory variables, ML algorithms demonstrate great promise in guiding physicians' decision-making.

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Levartovsky A, Eliakim R. Video Capsule Endoscopy Plays an Important Role in the Management of Crohn's Disease. Diagnostics (Basel) 2023;13:diagnostics13081507. [PMID: 37189607 DOI: 10.3390/diagnostics13081507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 05/17/2023] Open

Lenti MV, Scribano ML, Biancone L, Ciccocioppo R, Pugliese D, Pastorelli L, Fiorino G, Savarino E, Caprioli FA, Ardizzone S, Fantini MC, Tontini GE, Orlando A, Sampietro GM, Sturniolo GC, Monteleone G, Vecchi M, Kohn A, Daperno M, D’Incà R, Corazza GR, Di Sabatino A. Personalize, participate, predict, and prevent: 4Ps in inflammatory bowel disease. Front Med (Lausanne) 2023;10:1031998. [PMID: 37113615 PMCID: PMC10126747 DOI: 10.3389/fmed.2023.1031998] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 03/14/2023] [Indexed: 04/29/2023] Open

Affiliation(s)

Marco Vincenzo Lenti Department of Internal Medicine and Medical Therapeutics, University of Pavia, Pavia, Italy Department of Internal Medicine, San Matteo Hospital Foundation, Pavia, Italy
Maria Lia Scribano Villa Stuart, Multi-Speciality Clinic, Rome, Italy
Livia Biancone Unit of Gastroenterology, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
Rachele Ciccocioppo Gastroenterology Unit, Department of Medicine, A.O.U.I. Policlinico G.B. Rossi and University of Verona, Verona, Italy
Daniela Pugliese CEMAD Digestive Disease Center, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
Luca Pastorelli Liver and Gastroenterology Unit, ASST Santi Paolo e Carlo, Milan, Italy Department of Health Sciences, University of Milan, Milan, Italy
Gionata Fiorino IBD Unit, Ospedale San Camillo-Forlanini, Rome, Italy Department of Gastroenterology, San Raffaele Hospital and Vita-Salute San Raffaele University,, Milan, Italy
Edoardo Savarino Gastroenterology Unit, Department of Surgery, Oncology and Gastroenterology, University of Padua, Padua, Italy
Flavio Andrea Caprioli Gastroenterology and Endoscopy Unit, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Cà Granda, Ospedale Maggiore Policlinico and Università degli Studi di Milano, Milan, Italy
Sandro Ardizzone Gastroenterology and Digestive Endoscopy Unit, ASST Fatebenefratelli Sacco, Milan, Italy
Massimo Claudio Fantini Department of Medical Science and Public Health, University of Cagliari, Cagliari, Italy Gastroenterology Unit, Azienda Ospedaliero-Universitaria (AOU) di Cagliari, Cagliari, Italy
Gian Eugenio Tontini Department of Pathophysiology and Transplantation, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milano, Italy
Ambrogio Orlando Inflammatory Bowel Disease Unit, Azienda Ospedaliera Ospedali Riuniti "Villa Sofia-Cervello" Palermo, Palermo, Italy
Gianluca Matteo Sampietro Division of General and HBP Surgery, Rho Memorial Hospital, ASST Rhodense, Rho, Milano, Italy
Giacomo Carlo Sturniolo Gastroenterology Unit, Department of Surgery, Oncology and Gastroenterology, University of Padua, Padua, Italy
Giovanni Monteleone Unit of Gastroenterology, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
Maurizio Vecchi Gastroenterology and Endoscopy Unit, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Cà Granda, Ospedale Maggiore Policlinico and Università degli Studi di Milano, Milan, Italy
Anna Kohn Gastroenterology Operative Unit, Azienda Ospedaliera San Camillo-Forlanini FR, Rome, Italy
Marco Daperno Division of Gastroenterology, Ospedale Ordine Mauriziano di Torino, Turin, Italy
Renata D’Incà Department of Gastroenterology, San Raffaele Hospital and Vita-Salute San Raffaele University,, Milan, Italy
Gino Roberto Corazza Department of Internal Medicine and Medical Therapeutics, University of Pavia, Pavia, Italy Department of Internal Medicine, San Matteo Hospital Foundation, Pavia, Italy
Antonio Di Sabatino Department of Internal Medicine and Medical Therapeutics, University of Pavia, Pavia, Italy Department of Internal Medicine, San Matteo Hospital Foundation, Pavia, Italy

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Chiriac S, Sfarti CV, Minea H, Stanciu C, Cojocariu C, Singeap AM, Girleanu I, Cuciureanu T, Petrea O, Huiban L, Muzica CM, Zenovia S, Nastasa R, Stafie R, Rotaru A, Stratina E, Trifan A. Impaired Intestinal Permeability Assessed by Confocal Laser Endomicroscopy-A New Potential Therapeutic Target in Inflammatory Bowel Disease. Diagnostics (Basel) 2023;13:diagnostics13071230. [PMID: 37046447 PMCID: PMC10093200 DOI: 10.3390/diagnostics13071230] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/19/2023] [Accepted: 03/22/2023] [Indexed: 04/14/2023] Open

Affiliation(s)

Stefan Chiriac Department of Gastroenterology, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania Institute of Gastroenterology and Hepatology, "St. Spiridon" University Hospital, 700111 Iasi, Romania
Catalin Victor Sfarti Department of Gastroenterology, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania Institute of Gastroenterology and Hepatology, "St. Spiridon" University Hospital, 700111 Iasi, Romania
Horia Minea Department of Gastroenterology, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania Institute of Gastroenterology and Hepatology, "St. Spiridon" University Hospital, 700111 Iasi, Romania
Carol Stanciu Department of Gastroenterology, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania Institute of Gastroenterology and Hepatology, "St. Spiridon" University Hospital, 700111 Iasi, Romania
Camelia Cojocariu Department of Gastroenterology, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania Institute of Gastroenterology and Hepatology, "St. Spiridon" University Hospital, 700111 Iasi, Romania
Ana-Maria Singeap Department of Gastroenterology, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania Institute of Gastroenterology and Hepatology, "St. Spiridon" University Hospital, 700111 Iasi, Romania
Irina Girleanu Department of Gastroenterology, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania Institute of Gastroenterology and Hepatology, "St. Spiridon" University Hospital, 700111 Iasi, Romania
Tudor Cuciureanu Department of Gastroenterology, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania Institute of Gastroenterology and Hepatology, "St. Spiridon" University Hospital, 700111 Iasi, Romania
Oana Petrea Department of Gastroenterology, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania Institute of Gastroenterology and Hepatology, "St. Spiridon" University Hospital, 700111 Iasi, Romania
Laura Huiban Department of Gastroenterology, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania Institute of Gastroenterology and Hepatology, "St. Spiridon" University Hospital, 700111 Iasi, Romania
Cristina Maria Muzica Department of Gastroenterology, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania Institute of Gastroenterology and Hepatology, "St. Spiridon" University Hospital, 700111 Iasi, Romania
Sebastian Zenovia Department of Gastroenterology, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania Institute of Gastroenterology and Hepatology, "St. Spiridon" University Hospital, 700111 Iasi, Romania
Robert Nastasa Department of Gastroenterology, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania Institute of Gastroenterology and Hepatology, "St. Spiridon" University Hospital, 700111 Iasi, Romania
Remus Stafie Department of Gastroenterology, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania Institute of Gastroenterology and Hepatology, "St. Spiridon" University Hospital, 700111 Iasi, Romania
Adrian Rotaru Department of Gastroenterology, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania Institute of Gastroenterology and Hepatology, "St. Spiridon" University Hospital, 700111 Iasi, Romania
Ermina Stratina Department of Gastroenterology, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania Institute of Gastroenterology and Hepatology, "St. Spiridon" University Hospital, 700111 Iasi, Romania
Anca Trifan Department of Gastroenterology, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania Institute of Gastroenterology and Hepatology, "St. Spiridon" University Hospital, 700111 Iasi, Romania

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Murino A, Rimondi A. Automated artificial intelligence scoring systems for the endoscopic assessment of ulcerative colitis: How far are we from clinical application? Gastrointest Endosc 2023;97:347-349. [PMID: 36509572 DOI: 10.1016/j.gie.2022.10.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 10/04/2022] [Indexed: 12/15/2022]

Chang Y, Wang Z, Sun HB, Li YQ, Tang TY. Artificial Intelligence in Inflammatory Bowel Disease Endoscopy: Advanced Development and New Horizons. Gastroenterol Res Pract 2023;2023:3228832. [PMID: 37101782 PMCID: PMC10125749 DOI: 10.1155/2023/3228832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 11/28/2022] [Accepted: 12/02/2022] [Indexed: 04/28/2023] Open

Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. NPJ Digit Med 2022;5:184. [PMID: 36539473 PMCID: PMC9767933 DOI: 10.1038/s41746-022-00733-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open

Chierici M, Puica N, Pozzi M, Capistrano A, Donzella MD, Colangelo A, Osmani V, Jurman G. Automatically detecting Crohn's disease and Ulcerative Colitis from endoscopic imaging. BMC Med Inform Decis Mak 2022;22:300. [PMID: 36401328 PMCID: PMC9675066 DOI: 10.1186/s12911-022-02043-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 11/08/2022] [Indexed: 11/19/2022] Open

Leenhardt R, Koulaouzidis A, Histace A, Baatrup G, Beg S, Bourreille A, de Lange T, Eliakim R, Iakovidis D, Dam Jensen M, Keuchel M, Margalit Yehuda R, McNamara D, Mascarenhas M, Spada C, Segui S, Smedsrud P, Toth E, Tontini GE, Klang E, Dray X, Kopylov U. Key research questions for implementation of artificial intelligence in capsule endoscopy. Therap Adv Gastroenterol 2022;15:17562848221132683. [PMID: 36338789 PMCID: PMC9629556 DOI: 10.1177/17562848221132683] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 09/27/2022] [Indexed: 11/29/2022] Open

Affiliation(s)

Romain Leenhardt
Anastasios Koulaouzidis Department of Social Medicine and Public Health, Pomeranian Medical University, Szczecin, Poland,Department of Surgery, Odense University Hospital, Odense, Denmark,Department of Clinical research, University of Southern Denmark, Odense, Denmark
Aymeric Histace ETIS UMR 8051 (CY Paris Cergy University, ENSEA, CNRS), Cergy, France
Gunnar Baatrup Department of Surgery, Odense University Hospital, Odense, Denmark,Department of Clinical research, University of Southern Denmark, Odense, Denmark
Sabina Beg Department of Gastroenterology, Imperial College NHS Healthcare Trust, London, UK
Arnaud Bourreille Nantes Université, CHU Nantes, Institut des maladies de l’appareil digestif (IMAD), Hépato-gastroentérologie, Nantes, France
Thomas de Lange Department of Medicine and emergencies-Mölndal, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden,Department of Molecular and Clinical and Medicine, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden
Rami Eliakim Department of Gastroenterology, Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
Dimitris Iakovidis Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
Michael Dam Jensen Department of Internal Medicine, Section of Gastroenterology, Lillebaelt Hospital, Vejle, Denmark
Martin Keuchel Clinic for Internal Medicine, Agaplesion Bethesda Krankenhaus Bergedorf, Hamburg, Germany
Reuma Margalit Yehuda Department of Gastroenterology, Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
Deirdre McNamara Trinity Academic Gastroenterology Group, Department of Clinical Medicine, Tallaght Hospital, Trinity College Dublin, Dublin, Ireland
Miguel Mascarenhas Department of Gastroenterology, Centro Hospitalar São João, Porto, Portugal
Cristiano Spada Digestive Endoscopy Unit and Gastroenterology, Fondazione Poliambulanza, Brescia, Italy,Digestive Endoscopy Unit, Università Cattolica del Sacro Cuore, Rome, Italy
Santi Segui Department of Mathematics and Computer Science, Universitat de Barcelona, Barcelona, Spain
Pia Smedsrud Simula Metropolitan Centre for Digital Engineering, University of Oslo, Augere Medical AS, Oslo, Norway
Ervin Toth Department of Gastroenterology, Skåne University Hospital, Lund University, Malmö, Sweden
Gian Eugenio Tontini Department of Pathophysiology and Transplantation, University of Milan and Gastroenterology and Endoscopy Unit, Fondazione IRCCS Ca’Granda Ospedale Maggiore Policlinico, Milan, Italy
Eyal Klang Sheba ARC, Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
Xavier Dray Sorbonne Université, Centre of Digestive Endoscopy, Hôpital Saint-Antoine, AP-HP, Paris, France,ETIS UMR 8051 (CY Paris Cergy University, ENSEA, CNRS), Cergy, France
Uri Kopylov Department of Gastroenterology, Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel

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Stafford IS, Gosink MM, Mossotto E, Ennis S, Hauben M. A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation. Inflamm Bowel Dis 2022;28:1573-1583. [PMID: 35699597 PMCID: PMC9527612 DOI: 10.1093/ibd/izac115] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Indexed: 12/15/2022]

Yang LS, Perry E, Shan L, Wilding H, Connell W, Thompson AJ, Taylor ACF, Desmond PV, Holt BA. Clinical application and diagnostic accuracy of artificial intelligence in colonoscopy for inflammatory bowel disease: systematic review. Endosc Int Open 2022;10:E1004-E1013. [PMID: 35845028 PMCID: PMC9286774 DOI: 10.1055/a-1846-0642] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 05/02/2022] [Indexed: 12/15/2022] Open

Abstract

Background and aims Artificial intelligence (AI) technology is being evaluated for its potential to improve colonoscopic assessment of inflammatory bowel disease (IBD), particularly with computer-aided image classifiers. This review evaluates the clinical application and diagnostic test accuracy (DTA) of AI algorithms in colonoscopy for IBD. Methods A systematic review was performed on studies evaluating AI in colonoscopy of adult patients with IBD. MEDLINE, Embase, Emcare, PsycINFO, CINAHL, Cochrane Library and Clinicaltrials.gov databases were searched on 28 ^th April 2021 for English language articles published between January 1, 2000 and April 28, 2021. Risk of bias and applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Diagnostic accuracy was presented as median (interquartile range). Results Of 1029 records screened, nine studies with 7813 patients were included for review. AI was used to predict endoscopic and histologic disease activity in ulcerative colitis, and differentiation of Crohn's disease from Behcet's disease and intestinal tuberculosis. DTA of AI algorithms ranged between 52-91 %. The sensitivity and specificity for AI algorithms predicting endoscopic severity of disease were 78 % (range 72-83, interquartile range 5.5) and 91 % (range 86-96, interquartile range 5), respectively. Conclusions AI has been primarily used to assess disease activity in ulcerative colitis. The diagnostic performance is promising and suggests potential for other clinical application of AI in IBD colonoscopy such as dysplasia detection. However, current evidence is limited by retrospective data and models trained on still images only. Future prospective multicenter studies with full-motion videos are needed to replicate the real-world clinical setting.

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Elli L, Marinoni B, Sidhu R, Bojarski C, Branchi F, Tontini GE, Chetcuti Zammit S, Khater S, Eliakim R, Rondonotti E, Saurin JC, Bruno M, Buchkremer J, Cadoni S, Cavallaro F, Dray X, Ellul P, Urien IF, Keuchel M, Kopylov U, Koulaouzidis A, Leenhardt R, Baltes P, Beaumont H, Marmo C, McNamara D, Mussetto A, Nemeth A, Cuadrado Robles EP, Perrod G, Rahmi G, Riccioni ME, Robertson A, Spada C, Toth E, Triantafyllou K, Wurm Johansson G, Rimondi A. Nomenclature and Definition of Atrophic Lesions in Small Bowel Capsule Endoscopy: A Delphi Consensus Statement of the International CApsule endoscopy REsearch (I-CARE) Group. Diagnostics (Basel) 2022;12:diagnostics12071704. [PMID: 35885608 PMCID: PMC9325291 DOI: 10.3390/diagnostics12071704] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 06/25/2022] [Accepted: 07/07/2022] [Indexed: 11/16/2022] Open

Affiliation(s)

Luca Elli Department of Pathophysiology and Transplantation, Università degli Studi di Milano, 20122 Milan, Italy; Gastroenterology and Endoscopy Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy; (B.M.); (F.C.); (A.R.) Correspondence: ; Tel.: +39-02-55-03-33-64
Beatrice Marinoni Gastroenterology and Endoscopy Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy; (B.M.); (F.C.); (A.R.) Postgraduate Specialization in Gastrointestinal Diseases, Università degli Studi di Milano, 20122 Milan, Italy
Reena Sidhu Department of Infection, Immunity and Cardiovascular Diseases, Royal Hallamshire Hospital, University of Sheffield, Sheffield S10 2TN, UK;
Christian Bojarski Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, 10117 Berlin, Germany; (C.B.); (F.B.); (J.B.)
Federica Branchi Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, 10117 Berlin, Germany; (C.B.); (F.B.); (J.B.)
Gian Eugenio Tontini Department of Pathophysiology and Transplantation, Università degli Studi di Milano, 20122 Milan, Italy; Gastroenterology and Endoscopy Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy; (B.M.); (F.C.); (A.R.)
Stefania Chetcuti Zammit Department of Medicine, Division of Gastroenterology, Mater Dei Hospital, MSD 2090 Msida, Malta; (S.C.Z.); (P.E.)
Sherine Khater Department of Gastroenterology, Georges-Pompidou European Hospital, 75015 Paris, France; (S.K.); (E.P.C.R.); (G.P.); (G.R.)
Rami Eliakim Gastroenterology Department, Sheba Medical Center, Tel Aviv University, Tel Aviv 52621, Israel; (R.E.); (U.K.)
Emanuele Rondonotti Gastroenterology Department, Valduce Hospital, 22100 Como, Italy;
Jean Cristhophe Saurin Gastroenterology Department, Hospices Civils de Lyon-Centre Hospitalier Universitaire, 69002 Lyon, France;
Mauro Bruno University Division of Gastroenterology, City of Health and Science University Hospital, 10126 Turin, Italy;
Juliane Buchkremer Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, 10117 Berlin, Germany; (C.B.); (F.B.); (J.B.)
Sergio Cadoni Digestive Endoscopy Unit, CTO Hospital, 09016 Iglesias, Italy;
Flaminia Cavallaro Gastroenterology and Endoscopy Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy; (B.M.); (F.C.); (A.R.)
Xavier Dray Centre for Digestive Endoscopy, Sorbonne University, Saint Antoine Hospital, APHP, 75012 Paris, France; (X.D.); (R.L.)
Pierre Ellul Department of Medicine, Division of Gastroenterology, Mater Dei Hospital, MSD 2090 Msida, Malta; (S.C.Z.); (P.E.)
Ignacio Fernandez Urien Department of Gastroenterology, Complejo Hospitalario de Navarra, 31008 Pamplona, Spain;
Martin Keuchel Clinic for Internal Medicine, Agaplesion Bethesda Krankenhaus Bergedorf, Academic Teaching Hospital of the University of Hamburg, 21029 Hamburg, Germany; (M.K.); (P.B.)
Uri Kopylov Gastroenterology Department, Sheba Medical Center, Tel Aviv University, Tel Aviv 52621, Israel; (R.E.); (U.K.)
Anastasios Koulaouzidis Department of Medicine, Odense University Hospital (OUH)-Svendborg Sygehus, 5700 Svendborg, Denmark; Department of Clinical Research, University of Southern Denmark (SDU), 5230 Odense, Denmark Surgical Research Unit, OUH, 5000 Odense, Denmark
Romain Leenhardt Centre for Digestive Endoscopy, Sorbonne University, Saint Antoine Hospital, APHP, 75012 Paris, France; (X.D.); (R.L.)
Peter Baltes Clinic for Internal Medicine, Agaplesion Bethesda Krankenhaus Bergedorf, Academic Teaching Hospital of the University of Hamburg, 21029 Hamburg, Germany; (M.K.); (P.B.)
Hanneke Beaumont Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Location VU, 1118 Amsterdam, The Netherlands;
Clelia Marmo Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (C.M.); (M.E.R.); (C.S.)
Deirdre McNamara Trinity College Dublin, Tallaght University Hospital, D24 NR0A Dublin, Ireland;
Alessandro Mussetto Gastroenterology Unit, Santa Maria delle Croci Hospital, 48121 Ravenna, Italy;
Artur Nemeth Skåne University Hospital Malmö, Lund University, 221 00 Lund, Sweden; (A.N.); (E.T.); (G.W.J.)
Enrique Perez Cuadrado Robles Department of Gastroenterology, Georges-Pompidou European Hospital, 75015 Paris, France; (S.K.); (E.P.C.R.); (G.P.); (G.R.) Small Bowel Unit, Morales Meseguer Hospital, 30008 Murcia, Spain
Guillame Perrod Department of Gastroenterology, Georges-Pompidou European Hospital, 75015 Paris, France; (S.K.); (E.P.C.R.); (G.P.); (G.R.)
Gabriel Rahmi Department of Gastroenterology, Georges-Pompidou European Hospital, 75015 Paris, France; (S.K.); (E.P.C.R.); (G.P.); (G.R.)
Maria Elena Riccioni Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (C.M.); (M.E.R.); (C.S.)
Alexander Robertson Department of Gastroenterology, Western General Hospital, Edinburgh EH4 2XU, UK;
Cristiano Spada Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (C.M.); (M.E.R.); (C.S.) Digestive Endoscopy and Gastroenterology Unit, Poliambulanza Foundation, 25124 Brescia, Italy
Ervin Toth Skåne University Hospital Malmö, Lund University, 221 00 Lund, Sweden; (A.N.); (E.T.); (G.W.J.)
Konstantinos Triantafyllou Hepatogastroenterology Unit, 2nd Department of Propaedeutic Internal Medicine, Medical School, Attikon University General Hospital, National and Kapodistrian University, 157 72 Athens, Greece;
Gabriele Wurm Johansson Skåne University Hospital Malmö, Lund University, 221 00 Lund, Sweden; (A.N.); (E.T.); (G.W.J.)
Alessandro Rimondi Gastroenterology and Endoscopy Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy; (B.M.); (F.C.); (A.R.) Postgraduate Specialization in Gastrointestinal Diseases, Università degli Studi di Milano, 20122 Milan, Italy

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Kraszewski S, Szczurek W, Szymczak J, Reguła M, Neubauer K. Machine Learning Prediction Model for Inflammatory Bowel Disease Based on Laboratory Markers. Working Model in a Discovery Cohort Study. J Clin Med 2021;10:jcm10204745. [PMID: 34682868 PMCID: PMC8539616 DOI: 10.3390/jcm10204745] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 09/07/2021] [Accepted: 10/13/2021] [Indexed: 12/12/2022] Open