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Lei II, Koulaouzidis A, Schelde-Olesen B, Turvill J, Cortegoso Valdivia P, Rondonotti E, Plevris JN, Keuchel M, Saurin JC, Dray X, Brodersen JB, McAlindon M, Toth E, Robertson A, Arasaradnam R. Unifying terminology, reporting, and bowel preparation standards in colon capsule endoscopy: Nyborg Consensus. Endosc Int Open 2025; 13:a24955427. [PMID: 39958652 PMCID: PMC11827750 DOI: 10.1055/a-2495-5427] [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: 06/17/2024] [Accepted: 11/25/2024] [Indexed: 02/18/2025] Open
Abstract
Background and study aims Colon capsule endoscopy (CCE) is becoming increasingly popular in Europe. However, development of quality assurance and standardized terminology has not kept pace with clinical integration of this technology. As a result, there are significant variations in reporting standards, highlighting the need for a standardized terminology and framework. We used the RAND process to achieve a consensus of experts to determine the terminology in CCE, bowel cleansing assessment, and quality assurance reporting and future research priorities. Methods A panel comprising 14 European CCE experts evaluated 45 statements during the international REFLECT symposium (Nyborg, Denmark) through three survey rounds and face-to-face and virtual discussions in the initial two rounds. Participants anonymously rated statement appropriateness. Results Twenty-eight consensus statements were developed. Eight statements focus on consistent terminology for confirming CCE-detected polypoid and inflammatory colonic lesions with colonoscopy. To ensure standardization and quality assurance, 13 mandatory fields were recommended for inclusion in a CCE report. Three endorsed reporting methodologies were suggested, emphasizing prompt notification for suspected malignant findings, recommending a generic disclaimer regarding stomach and small bowel visualization intentions, and establishing reporting timelines at an interdepartmental level based on urgency. Four bowel preparation scale-related statements led to the recommendation to adoptithe Colon Capsule CLEansing Assessment and Reporting (CC-CLEAR) scale as the preferred scale. Conclusions This study established a framework for terminology, reporting, and assessment of bowel cleansing for CCE. Future research should focus on optimizing bowel preparation regimens and exploring artificial intelligence applications in CCE.
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Affiliation(s)
- Ian Io Lei
- School of Medicine, University of Warwick, Coventry, United Kingdom of Great Britain and Northern Ireland
- University Hospital Coventry, Coventry, United Kingdom of Great Britain and Northern Ireland
| | - Anastasios Koulaouzidis
- Surgical Research Unit, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Department of Social Medicine and Public Health, Pomeranian Medical University in Szczecin, Szczecin, Poland
- Department of Medicine, Svendborg Sygehus, Svendborg, Denmark
| | | | - James Turvill
- Department of Gastroenterology, York Hospitals NHS Foundation Trust, York, United Kingdom of Great Britain and Northern Ireland
| | | | | | - John N. Plevris
- Gastroenterology/Hepatology, University of Edinburgh, edinburgh, United Kingdom of Great Britain and Northern Ireland
| | - Martin Keuchel
- Chefarzt der Klinik für Innere Medizin, Bethesda Krankenhaus Bergedorf gemeinnutzige GmbH, Hamburg, Germany
| | | | - Xavier Dray
- Endoscopy, Hôpital Saint-Antoine, APHP, Sorbonne Université, Paris, France
| | - Jacob Broder Brodersen
- Internal Medicine, Section of Gastroenterology, South West Jutland Hospital Medical Library, Esbjerg, Denmark
| | - Mark McAlindon
- Academic Unit of Gastroenterology and Hepatology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom of Great Britain and Northern Ireland
| | - Ervin Toth
- Endoscopy Unit, Department of Gastroenterology, Skane University Hospital, Malmo, Sweden
| | - Alexander Robertson
- Department of Digestive Diseases, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom of Great Britain and Northern Ireland
| | - Ramesh Arasaradnam
- Medical school, University of Warwick, Coventry, United Kingdom of Great Britain and Northern Ireland
- Institute of Precision Diagnostics & Translational Medicine, University Hospital Coventry, Coventry, United Kingdom of Great Britain and Northern Ireland
- Leicester Cancer Centre, University of Leicester, Leicester, United Kingdom of Great Britain and Northern Ireland
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Mascarenhas M, Mendes F, Ribeiro T, Afonso J, Marílio Cardoso P, Martins M, Cardoso H, Andrade P, Ferreira J, Mascarenhas Saraiva M, Macedo G. Deep Learning and Minimally Invasive Endoscopy: Panendoscopic Detection of Pleomorphic Lesions. GE PORTUGUESE JOURNAL OF GASTROENTEROLOGY 2024; 31:408-418. [PMID: 39633912 PMCID: PMC11614440 DOI: 10.1159/000539837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/11/2024] [Indexed: 12/07/2024]
Abstract
Introduction Capsule endoscopy (CE) is a minimally invasive exam suitable of panendoscopic evaluation of the gastrointestinal (GI) tract. Nevertheless, CE is time-consuming with suboptimal diagnostic yield in the upper GI tract. Convolutional neural networks (CNN) are human brain architecture-based models suitable for image analysis. However, there is no study about their role in capsule panendoscopy. Methods Our group developed an artificial intelligence (AI) model for panendoscopic automatic detection of pleomorphic lesions (namely vascular lesions, protuberant lesions, hematic residues, ulcers, and erosions). 355,110 images (6,977 esophageal, 12,918 gastric, 258,443 small bowel, 76,772 colonic) from eight different CE and colon CE (CCE) devices were divided into a training and validation dataset in a patient split design. The model classification was compared to three CE experts' classification. The model's performance was evaluated by its sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the precision-recall curve. Results The binary esophagus CNN had a diagnostic accuracy for pleomorphic lesions of 83.6%. The binary gastric CNN identified pleomorphic lesions with a 96.6% accuracy. The undenary small bowel CNN distinguished pleomorphic lesions with different hemorrhagic potentials with 97.6% accuracy. The trinary colonic CNN (detection and differentiation of normal mucosa, pleomorphic lesions, and hematic residues) had 94.9% global accuracy. Discussion/Conclusion We developed the first AI model for panendoscopic automatic detection of pleomorphic lesions in both CE and CCE from multiple brands, solving a critical interoperability technological challenge. Deep learning-based tools may change the landscape of minimally invasive capsule panendoscopy.
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Affiliation(s)
- Miguel Mascarenhas
- Department of Gastroenterology, Precision Medicine Unit, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Francisco Mendes
- Department of Gastroenterology, Precision Medicine Unit, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Tiago Ribeiro
- Department of Gastroenterology, Precision Medicine Unit, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, Precision Medicine Unit, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Pedro Marílio Cardoso
- Department of Gastroenterology, Precision Medicine Unit, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Miguel Martins
- Department of Gastroenterology, Precision Medicine Unit, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Hélder Cardoso
- Department of Gastroenterology, Precision Medicine Unit, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Patrícia Andrade
- Department of Gastroenterology, Precision Medicine Unit, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- Digestive Artificial Intelligence Development, Porto, Portugal
| | | | - Guilherme Macedo
- Department of Gastroenterology, Precision Medicine Unit, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
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Agudo Castillo B, Mascarenhas M, Martins M, Mendes F, de la Iglesia D, Costa AMMPD, Esteban Fernández-Zarza C, González-Haba Ruiz M. Advancements in biliopancreatic endoscopy - A comprehensive review of artificial intelligence in EUS and ERCP. REVISTA ESPANOLA DE ENFERMEDADES DIGESTIVAS 2024; 116:613-622. [PMID: 38832589 DOI: 10.17235/reed.2024.10456/2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
The development and implementation of artificial intelligence (AI), particularly deep learning (DL) models, has generated significant interest across various fields of gastroenterology. While research in luminal endoscopy has seen rapid translation to clinical practice with approved AI devices, its potential extends far beyond, offering promising benefits for biliopancreatic endoscopy like optical characterization of strictures during cholangioscopy or detection and classification of pancreatic lesions during diagnostic endoscopic ultrasound (EUS). This narrative review provides an up-to-date of the latest literature and available studies in this field. Serving as a comprehensive guide to the current landscape of AI in biliopancreatic endoscopy, emphasizing technological advancements, main applications, ethical considerations, and future directions for research and clinical implementation.
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Affiliation(s)
| | | | - Miguel Martins
- Gastroenterology, Centro Hospitalar Universitário de São João
| | - Francisco Mendes
- Gastroenterology, Centro Hospitalar Universitário de São João, Portugal
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Shah SA, Taj I, Usman SM, Hassan Shah SN, Imran AS, Khalid S. A hybrid approach of vision transformers and CNNs for detection of ulcerative colitis. Sci Rep 2024; 14:24771. [PMID: 39433818 PMCID: PMC11494132 DOI: 10.1038/s41598-024-75901-4] [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: 07/01/2024] [Accepted: 10/09/2024] [Indexed: 10/23/2024] Open
Abstract
Ulcerative Colitis is an Inflammatory Bowel disease caused by a variety of factors that lead to a serious impact on the quality of life of the patients if left untreated. Due to complexities in the identification procedures of this disease, the treatment timeline and quality can be severely affected, leading to further consequences for the sufferer. The difficulties in identification are due to high patients to healthcare professionals ratio. Researchers have proposed variety of machine/deep learning methods for automated detection of ulcerative colitis, however, several challenges exists including class imbalance problem, comprehensive feature extraction and accurate classification. We propose a novel method for accurate detection of ulcerative colitis with augmentation techniques to overcome class imbalance issue, a comprehensive feature vector extraction using custom architecture of Vision Transformer (ViT) and accurate classification using customized Convolutional Neural Network (CNN). We used the TMC-UCM and LIMUC datasets in this research for training and testing of proposed method and achieved accuracy of 90% with AUC-ROC scores of 0.91, 0.81, 0.94, and 0.94 for the endoscopic classes of Mayo 0, Mayo 1, Mayo 2, and Mayo 3 respectively. We have compared the proposed method with existing state of the art methods and conclude that the proposed method outperforms the existing methods.
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Affiliation(s)
- Syed Abdullah Shah
- Department of Creative Technologies, Faculty of Computing and Artificial Intelligence, Air University, Islamabad, 44000, Pakistan
| | - Imran Taj
- College of Interdisciplinary Studies, Zayed University, 144534, Abu Dhabi, United Arab Emirates
| | - Syed Muhammad Usman
- Department of Computer Science, Bahria School of Engineering and Applied Sciences, Bahria University, Islamabad, 44000, Pakistan
| | - Syed Nehal Hassan Shah
- Department of Creative Technologies, Faculty of Computing and Artificial Intelligence, Air University, Islamabad, 44000, Pakistan
| | - Ali Shariq Imran
- Department of Computer Science, Norwegian University of Science and Technology, Gjøvik, 2815, Norway.
| | - Shehzad Khalid
- Department of Computer Engineering, Bahria School of Engineering and Applied Sciences, Bahria University, Islamabad, 44000, Pakistan
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Habe TT, Haataja K, Toivanen P. Review of Deep Learning Performance in Wireless Capsule Endoscopy Images for GI Disease Classification. F1000Res 2024; 13:201. [PMID: 39464781 PMCID: PMC11503939 DOI: 10.12688/f1000research.145950.1] [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] [Accepted: 09/18/2024] [Indexed: 10/29/2024] Open
Abstract
Wireless capsule endoscopy is a non-invasive medical imaging modality used for diagnosing and monitoring digestive tract diseases. However, the analysis of images obtained from wireless capsule endoscopy is a challenging task, as the images are of low resolution and often contain a large number of artifacts. In recent years, deep learning has shown great promise in the analysis of medical images, including wireless capsule endoscopy images. This paper provides a review of the current trends and future directions in deep learning for wireless capsule endoscopy. We focus on the recent advances in transfer learning, attention mechanisms, multi-modal learning, automated lesion detection, interpretability and explainability, data augmentation, and edge computing. We also highlight the challenges and limitations of current deep learning methods and discuss the potential future directions for the field. Our review provides insights into the ongoing research and development efforts in the field of deep learning for wireless capsule endoscopy, and can serve as a reference for researchers, clinicians, and engineers working in this area inspection process.
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Affiliation(s)
- Tsedeke Temesgen Habe
- School of Computing, Faculty of Science, Forestry and Technology, University of Eastern Finland, Joensuu, North Karelia, 70211, Finland
| | - Keijo Haataja
- School of Computing, Faculty of Science, Forestry and Technology, University of Eastern Finland, Joensuu, North Karelia, 70211, Finland
| | - Pekka Toivanen
- School of Computing, Faculty of Science, Forestry and Technology, University of Eastern Finland, Joensuu, North Karelia, 70211, Finland
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Mascarenhas M, Martins M, Ribeiro T, Afonso J, Cardoso P, Mendes F, Cardoso H, Almeida R, Ferreira J, Fonseca J, Macedo G. Software as a Medical Device (SaMD) in Digestive Healthcare: Regulatory Challenges and Ethical Implications. Diagnostics (Basel) 2024; 14:2100. [PMID: 39335779 PMCID: PMC11431531 DOI: 10.3390/diagnostics14182100] [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: 07/24/2024] [Revised: 08/29/2024] [Accepted: 09/05/2024] [Indexed: 09/30/2024] Open
Abstract
The growing integration of software in healthcare, particularly the rise of standalone software as a medical device (SaMD), is transforming digestive medicine, a field heavily reliant on medical imaging for both diagnosis and therapeutic interventions. This narrative review aims to explore the impact of SaMD on digestive healthcare, focusing on the evolution of these tools and their regulatory and ethical challenges. Our analysis highlights the exponential growth of SaMD in digestive healthcare, driven by the need for precise diagnostic tools and personalized treatment strategies. This rapid advancement, however, necessitates the parallel development of a robust regulatory framework to ensure SaMDs are transparent and deliver universal clinical benefits without the introduction of bias or harm. In addition, the discussion highlights the importance of adherence to the FAIR principles for data management-findability, accessibility, interoperability, and reusability. However, enhanced accessibility and interoperability require rigorous protocols to ensure compliance with data protection guidelines and adequate data security, both of which are crucial for effective integration of SaMDs into clinical workflows. In conclusion, while SaMDs hold significant promise for improving patients' outcomes in digestive medicine, their successful integration into clinical workflow depends on rigorous data protection protocols and clinical validation. Future directions include the need for adequate clinical and real-world studies to demonstrate that these devices are safe and well-suited to healthcare settings.
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Affiliation(s)
- Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine of University of Porto, 4200 427 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
| | - Hélder Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
| | - Rute Almeida
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine of University of Porto, 4200 427 Porto, Portugal
| | - João Ferreira
- Department of Mechanic Engineering, Faculty of Engineering of University of Porto, 4200 427 Porto, Portugal
- DigestAID-Digestive Artificial Intelligence Development, 4200 427 Porto, Portugal
| | - João Fonseca
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine of University of Porto, 4200 427 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
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Mota J, João Almeida M, Mendes F, Martins M, Ribeiro T, Afonso J, Cardoso P, Cardoso H, Andrade P, Ferreira J, Macedo G, Mascarenhas M. A Comprehensive Review of Artificial Intelligence and Colon Capsule Endoscopy: Opportunities and Challenges. Diagnostics (Basel) 2024; 14:2072. [PMID: 39335751 PMCID: PMC11431528 DOI: 10.3390/diagnostics14182072] [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: 07/28/2024] [Revised: 08/28/2024] [Accepted: 08/30/2024] [Indexed: 09/30/2024] Open
Abstract
Colon capsule endoscopy (CCE) enables a comprehensive, non-invasive, and painless evaluation of the colon, although it still has limited indications. The lengthy reading times hinder its wider implementation, a drawback that could potentially be overcome through the integration of artificial intelligence (AI) models. Studies employing AI, particularly convolutional neural networks (CNNs), demonstrate great promise in using CCE as a viable option for detecting certain diseases and alterations in the colon, compared to other methods like colonoscopy. Additionally, employing AI models in CCE could pave the way for a minimally invasive panenteric or even panendoscopic solution. This review aims to provide a comprehensive summary of the current state-of-the-art of AI in CCE while also addressing the challenges, both technical and ethical, associated with broadening indications for AI-powered CCE. Additionally, it also gives a brief reflection of the potential environmental advantages of using this method compared to alternative ones.
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Affiliation(s)
- Joana Mota
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Maria João Almeida
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Helder Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal
| | - Patricia Andrade
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
- Digestive Artificial Intelligence Development, 4200-135 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal
| | - Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal
- ManopH Gastroenterology Clinic, 4000-432 Porto, Portugal
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Mascarenhas M, Martins M, Afonso J, Ribeiro T, Cardoso P, Mendes F, Andrade P, Cardoso H, Mascarenhas-Saraiva M, Ferreira J, Macedo G. Deep learning and capsule endoscopy: Automatic multi-brand and multi-device panendoscopic detection of vascular lesions. Endosc Int Open 2024; 12:E570-E578. [PMID: 38654967 PMCID: PMC11039033 DOI: 10.1055/a-2236-7849] [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: 05/31/2023] [Accepted: 12/21/2023] [Indexed: 04/26/2024] Open
Abstract
Background and study aims Capsule endoscopy (CE) is commonly used as the initial exam for suspected mid-gastrointestinal bleeding after normal upper and lower endoscopy. Although the assessment of the small bowel is the primary focus of CE, detecting upstream or downstream vascular lesions may also be clinically significant. This study aimed to develop and test a convolutional neural network (CNN)-based model for panendoscopic automatic detection of vascular lesions during CE. Patients and methods A multicentric AI model development study was based on 1022 CE exams. Our group used 34655 frames from seven types of CE devices, of which 11091 were considered to have vascular lesions (angiectasia or varices) after triple validation. We divided data into a training and a validation set, and the latter was used to evaluate the model's performance. At the time of division, all frames from a given patient were assigned to the same dataset. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and an area under the precision-recall curve (AUC-PR). Results Sensitivity and specificity were 86.4% and 98.3%, respectively. PPV was 95.2%, while the NPV was 95.0%. Overall accuracy was 95.0%. The AUC-PR value was 0.96. The CNN processed 115 frames per second. Conclusions This is the first proof-of-concept artificial intelligence deep learning model developed for pan-endoscopic automatic detection of vascular lesions during CE. The diagnostic performance of this CNN in multi-brand devices addresses an essential issue of technological interoperability, allowing it to be replicated in multiple technological settings.
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Affiliation(s)
- Miguel Mascarenhas
- Gastroenterology, Centro Hospitalar Universitário de São João, Porto, Portugal
| | | | - João Afonso
- Gastroenterology, Centro Hospitalar Universitário de São João, Porto, Portugal
| | - Tiago Ribeiro
- Gastroenterology, Centro Hospitalar Universitário de São João, Porto, Portugal
| | - Pedro Cardoso
- Gastroenterology, Centro Hospitalar Universitário de São João, Porto, Portugal
| | - Franscisco Mendes
- Gastroenterology, Centro Hospitalar Universitário de São João, Porto, Portugal
| | - Patrícia Andrade
- Gastroenterology, Centro Hospitalar Universitário de São João, Porto, Portugal
| | - Helder Cardoso
- Gastroenterology, Centro Hospitalar Universitário de São João, Porto, Portugal
| | | | - João Ferreira
- Department of Mechanical Engineering., University of Porto Faculty of Engineering, Porto, Portugal
| | - Guilherme Macedo
- Gastroenterology, Centro Hospitalar Universitário de São João, Porto, Portugal
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George AA, Tan JL, Kovoor JG, Lee A, Stretton B, Gupta AK, Bacchi S, George B, Singh R. Artificial intelligence in capsule endoscopy: development status and future expectations. MINI-INVASIVE SURGERY 2024. [DOI: 10.20517/2574-1225.2023.102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2025]
Abstract
In this review, we aim to illustrate the state-of-the-art artificial intelligence (AI) applications in the field of capsule endoscopy. AI has made significant strides in gastrointestinal imaging, particularly in capsule endoscopy - a non-invasive procedure for capturing gastrointestinal tract images. However, manual analysis of capsule endoscopy videos is labour-intensive and error-prone, prompting the development of automated computational algorithms and AI models. While currently serving as a supplementary observer, AI has the capacity to evolve into an autonomous, integrated reading system, potentially significantly reducing capsule reading time while surpassing human accuracy. We searched Embase, Pubmed, Medline, and Cochrane databases from inception to 06 Jul 2023 for studies investigating the use of AI for capsule endoscopy and screened retrieved records for eligibility. Quantitative and qualitative data were extracted and synthesised to identify current themes. In the search, 824 articles were collected, and 291 duplicates and 31 abstracts were deleted. After a double-screening process and full-text review, 106 publications were included in the review. Themes pertaining to AI for capsule endoscopy included active gastrointestinal bleeding, erosions and ulcers, vascular lesions and angiodysplasias, polyps and tumours, inflammatory bowel disease, coeliac disease, hookworms, bowel prep assessment, and multiple lesion detection. This review provides current insights into the impact of AI on capsule endoscopy as of 2023. AI holds the potential for faster and precise readings and the prospect of autonomous image analysis. However, careful consideration of diagnostic requirements and potential challenges is crucial. The untapped potential within vision transformer technology hints at further evolution and even greater patient benefit.
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Rosa B, Cotter J. Capsule endoscopy and panendoscopy: A journey to the future of gastrointestinal endoscopy. World J Gastroenterol 2024; 30:1270-1279. [PMID: 38596501 PMCID: PMC11000081 DOI: 10.3748/wjg.v30.i10.1270] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 01/22/2024] [Accepted: 02/21/2024] [Indexed: 03/14/2024] Open
Abstract
In 2000, the small bowel capsule revolutionized the management of patients with small bowel disorders. Currently, the technological development achieved by the new models of double-headed endoscopic capsules, as miniaturized devices to evaluate the small bowel and colon [pan-intestinal capsule endoscopy (PCE)], makes this non-invasive procedure a disruptive concept for the management of patients with digestive disorders. This technology is expected to identify which patients will require conventional invasive endoscopic procedures (colonoscopy or balloon-assisted enteroscopy), based on the lesions detected by the capsule, i.e., those with an indication for biopsies or endoscopic treatment. The use of PCE in patients with inflammatory bowel diseases, namely Crohn's disease, as well as in patients with iron deficiency anaemia and/or overt gastrointestinal (GI) bleeding, after a non-diagnostic upper endoscopy (esophagogastroduodenoscopy), enables an effective, safe and comfortable way to identify patients with relevant lesions, who should undergo subsequent invasive endoscopic procedures. The recent development of magnetically controlled capsule endoscopy to evaluate the upper GI tract, is a further step towards the possibility of an entirely non-invasive assessment of all the segments of the digestive tract, from mouth-to-anus, meeting the expectations of the early developers of capsule endoscopy.
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Affiliation(s)
- Bruno Rosa
- Department of Gastroenterology, Hospital da Senhora da Oliveira, Guimarães 4835-044, Portugal
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga 4710-057, Portugal
- ICVS/3B's, PT Government Associate Laboratory, Braga 4710-057, Portugal
| | - José Cotter
- Department of Gastroenterology, Hospital da Senhora da Oliveira, Guimarães 4835-044, Portugal
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga 4710-057, Portugal
- ICVS/3B's, PT Government Associate Laboratory, Braga 4710-057, Portugal
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11
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Jiang B, Dorosan M, Leong JWH, Ong MEH, Lam SSW, Ang TL. Development and validation of a deep learning system for detection of small bowel pathologies in capsule endoscopy: a pilot study in a Singapore institution. Singapore Med J 2024; 65:133-140. [PMID: 38527297 PMCID: PMC11060635 DOI: 10.4103/singaporemedj.smj-2023-187] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 12/10/2023] [Indexed: 03/27/2024]
Abstract
INTRODUCTION Deep learning models can assess the quality of images and discriminate among abnormalities in small bowel capsule endoscopy (CE), reducing fatigue and the time needed for diagnosis. They serve as a decision support system, partially automating the diagnosis process by providing probability predictions for abnormalities. METHODS We demonstrated the use of deep learning models in CE image analysis, specifically by piloting a bowel preparation model (BPM) and an abnormality detection model (ADM) to determine frame-level view quality and the presence of abnormal findings, respectively. We used convolutional neural network-based models pretrained on large-scale open-domain data to extract spatial features of CE images that were then used in a dense feed-forward neural network classifier. We then combined the open-source Kvasir-Capsule dataset (n = 43) and locally collected CE data (n = 29). RESULTS Model performance was compared using averaged five-fold and two-fold cross-validation for BPMs and ADMs, respectively. The best BPM model based on a pre-trained ResNet50 architecture had an area under the receiver operating characteristic and precision-recall curves of 0.969±0.008 and 0.843±0.041, respectively. The best ADM model, also based on ResNet50, had top-1 and top-2 accuracies of 84.03±0.051 and 94.78±0.028, respectively. The models could process approximately 200-250 images per second and showed good discrimination on time-critical abnormalities such as bleeding. CONCLUSION Our pilot models showed the potential to improve time to diagnosis in CE workflows. To our knowledge, our approach is unique to the Singapore context. The value of our work can be further evaluated in a pragmatic manner that is sensitive to existing clinician workflow and resource constraints.
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Affiliation(s)
- Bochao Jiang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore
| | - Michael Dorosan
- Health Services Research Centre, Singapore Health Services Pte Ltd, Singapore
| | - Justin Wen Hao Leong
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore
| | - Marcus Eng Hock Ong
- Health Services and Systems Research, Duke-NUS Medical School, Singapore
- Department of Emergency Medicine, Singapore General Hospital, Singapore
| | - Sean Shao Wei Lam
- Health Services Research Centre, Singapore Health Services Pte Ltd, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore
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12
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Mendes F, Mascarenhas M, Ribeiro T, Afonso J, Cardoso P, Martins M, Cardoso H, Andrade P, Ferreira JPS, Mascarenhas Saraiva M, Macedo G. Artificial Intelligence and Panendoscopy-Automatic Detection of Clinically Relevant Lesions in Multibrand Device-Assisted Enteroscopy. Cancers (Basel) 2024; 16:208. [PMID: 38201634 PMCID: PMC10778030 DOI: 10.3390/cancers16010208] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 12/27/2023] [Accepted: 12/28/2023] [Indexed: 01/12/2024] Open
Abstract
Device-assisted enteroscopy (DAE) is capable of evaluating the entire gastrointestinal tract, identifying multiple lesions. Nevertheless, DAE's diagnostic yield is suboptimal. Convolutional neural networks (CNN) are multi-layer architecture artificial intelligence models suitable for image analysis, but there is a lack of studies about their application in DAE. Our group aimed to develop a multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. In total, 338 exams performed in two specialized centers were retrospectively evaluated, with 152 single-balloon enteroscopies (Fujifilm®, Porto, Portugal), 172 double-balloon enteroscopies (Olympus®, Porto, Portugal) and 14 motorized spiral enteroscopies (Olympus®, Porto, Portugal); then, 40,655 images were divided in a training dataset (90% of the images, n = 36,599) and testing dataset (10% of the images, n = 4066) used to evaluate the model. The CNN's output was compared to an expert consensus classification. The model was evaluated by its sensitivity, specificity, positive (PPV) and negative predictive values (NPV), accuracy and area under the precision recall curve (AUC-PR). The CNN had an 88.9% sensitivity, 98.9% specificity, 95.8% PPV, 97.1% NPV, 96.8% accuracy and an AUC-PR of 0.97. Our group developed the first multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. The development of accurate deep learning models is of utmost importance for increasing the diagnostic yield of DAE-based panendoscopy.
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Affiliation(s)
- Francisco Mendes
- Alameda Professor Hernâni Monteiro, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (T.R.); (P.C.); (M.M.); (P.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4050-345 Porto, Portugal
| | - Miguel Mascarenhas
- Alameda Professor Hernâni Monteiro, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (T.R.); (P.C.); (M.M.); (P.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Tiago Ribeiro
- Alameda Professor Hernâni Monteiro, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (T.R.); (P.C.); (M.M.); (P.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João Afonso
- Alameda Professor Hernâni Monteiro, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (T.R.); (P.C.); (M.M.); (P.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Alameda Professor Hernâni Monteiro, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (T.R.); (P.C.); (M.M.); (P.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Martins
- Alameda Professor Hernâni Monteiro, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (T.R.); (P.C.); (M.M.); (P.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4050-345 Porto, Portugal
| | - Hélder Cardoso
- Alameda Professor Hernâni Monteiro, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (T.R.); (P.C.); (M.M.); (P.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Patrícia Andrade
- Alameda Professor Hernâni Monteiro, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (T.R.); (P.C.); (M.M.); (P.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João P. S. Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal;
- DigestAID—Digestive Artificial Intelligence Development, R. Alfredo Allen n°. 455/461, 4200-135 Porto, Portugal
| | | | - Guilherme Macedo
- Alameda Professor Hernâni Monteiro, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (F.M.); (T.R.); (P.C.); (M.M.); (P.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
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13
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Mascarenhas M, Martins M, Afonso J, Ribeiro T, Cardoso P, Mendes F, Andrade P, Cardoso H, Ferreira J, Macedo G. The Future of Minimally Invasive Capsule Panendoscopy: Robotic Precision, Wireless Imaging and AI-Driven Insights. Cancers (Basel) 2023; 15:5861. [PMID: 38136403 PMCID: PMC10742312 DOI: 10.3390/cancers15245861] [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: 11/04/2023] [Revised: 12/04/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023] Open
Abstract
In the early 2000s, the introduction of single-camera wireless capsule endoscopy (CE) redefined small bowel study. Progress continued with the development of double-camera devices, first for the colon and rectum, and then, for panenteric assessment. Advancements continued with magnetic capsule endoscopy (MCE), particularly when assisted by a robotic arm, designed to enhance gastric evaluation. Indeed, as CE provides full visualization of the entire gastrointestinal (GI) tract, a minimally invasive capsule panendoscopy (CPE) could be a feasible alternative, despite its time-consuming nature and learning curve, assuming appropriate bowel cleansing has been carried out. Recent progress in artificial intelligence (AI), particularly in the development of convolutional neural networks (CNN) for CE auxiliary reading (detecting and diagnosing), may provide the missing link in fulfilling the goal of establishing the use of panendoscopy, although prospective studies are still needed to validate these models in actual clinical scenarios. Recent CE advancements will be discussed, focusing on the current evidence on CNN developments, and their real-life implementation potential and associated ethical challenges.
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Affiliation(s)
- Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (M.M.); (J.A.); (T.R.); (P.C.); (F.M.); (P.A.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (M.M.); (J.A.); (T.R.); (P.C.); (F.M.); (P.A.); (H.C.); (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; (M.M.); (J.A.); (T.R.); (P.C.); (F.M.); (P.A.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (M.M.); (J.A.); (T.R.); (P.C.); (F.M.); (P.A.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (M.M.); (J.A.); (T.R.); (P.C.); (F.M.); (P.A.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (M.M.); (J.A.); (T.R.); (P.C.); (F.M.); (P.A.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Patrícia Andrade
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (M.M.); (J.A.); (T.R.); (P.C.); (F.M.); (P.A.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal
| | - Helder Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (M.M.); (J.A.); (T.R.); (P.C.); (F.M.); (P.A.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal
| | - João Ferreira
- Department of Mechanic Engineering, Faculty of Engineering, University of Porto, 4200-065 Porto, Portugal;
- DigestAID—Digestive Artificial Intelligence Development, 455/461, 4200-135 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (M.M.); (J.A.); (T.R.); (P.C.); (F.M.); (P.A.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal
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14
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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
Abstract
The surge in the implementation of artificial intelligence (AI) in recent years has permeated many aspects of our life, and health care is no exception. Whereas this technology can offer clear benefits, some of the problems associated with its use have also been recognised and brought into question, for example, its environmental impact. In a similar fashion, health care also has a significant environmental impact, and it requires a considerable source of greenhouse gases. Whereas efforts are being made to reduce the footprint of AI tools, here, we were specifically interested in how employing AI tools in gastroenterology departments, and in particular in conjunction with capsule endoscopy, can reduce the carbon footprint associated with digestive health care while offering improvements, particularly in terms of diagnostic accuracy. We address the different ways that leveraging AI applications can reduce the carbon footprint associated with all types of capsule endoscopy examinations. Moreover, we contemplate how the incorporation of other technologies, such as blockchain technology, into digestive health care can help ensure the sustainability of this clinical speciality and by extension, health care in general.
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Affiliation(s)
- Miguel Mascarenhas
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal; (T.R.); (J.A.); (P.C.); (M.M.)
- WGO Training Center, 4200-437 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal; (T.R.); (J.A.); (P.C.); (M.M.)
- WGO Training Center, 4200-437 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal; (T.R.); (J.A.); (P.C.); (M.M.)
- WGO Training Center, 4200-437 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal; (T.R.); (J.A.); (P.C.); (M.M.)
- WGO Training Center, 4200-437 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal; (T.R.); (J.A.); (P.C.); (M.M.)
- WGO Training Center, 4200-437 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal; (T.R.); (J.A.); (P.C.); (M.M.)
- WGO Training Center, 4200-437 Porto, Portugal
| | - João Ferreira
- Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal;
| | - Guilherme Macedo
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal; (T.R.); (J.A.); (P.C.); (M.M.)
- WGO Training Center, 4200-437 Porto, Portugal
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15
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Musha A, Hasnat R, Mamun AA, Ping EP, Ghosh T. Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:7170. [PMID: 37631707 PMCID: PMC10459126 DOI: 10.3390/s23167170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023]
Abstract
Capsule endoscopy (CE) is a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. However, CE captures a huge number of image frames, constituting a time-consuming and tedious task for medical experts to manually inspect. To address this issue, researchers have focused on computer-aided bleeding detection systems to automatically identify bleeding in real time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories (Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect) for all original publications on computer-aided bleeding detection published between 2001 and 2023. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) methodology was used to perform the review, and 147 full texts of scientific papers were reviewed. The contributions of this paper are: (I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified; (II) the available state-of-the-art computer-aided bleeding detection algorithms, including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers, are discussed; and (III) the most effective algorithms for practical use are identified. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research.
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Affiliation(s)
- Ahmmad Musha
- Department of Electrical and Electronic Engineering, Pabna University of Science and Technology, Pabna 6600, Bangladesh; (A.M.); (R.H.)
| | - Rehnuma Hasnat
- Department of Electrical and Electronic Engineering, Pabna University of Science and Technology, Pabna 6600, Bangladesh; (A.M.); (R.H.)
| | - Abdullah Al Mamun
- Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia;
| | - Em Poh Ping
- Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia;
| | - Tonmoy Ghosh
- Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA;
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Bousis D, Verras GI, Bouchagier K, Antzoulas A, Panagiotopoulos I, Katinioti A, Kehagias D, Kaplanis C, Kotis K, Anagnostopoulos CN, Mulita F. The role of deep learning in diagnosing colorectal cancer. PRZEGLAD GASTROENTEROLOGICZNY 2023; 18:266-273. [PMID: 37937113 PMCID: PMC10626379 DOI: 10.5114/pg.2023.129494] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 02/24/2023] [Indexed: 11/09/2023]
Abstract
Colon cancer is a major public health issue, affecting a growing number of individuals worldwide. Proper and early diagnosis of colon cancer is the necessary first step toward effective treatment and/or prevention of future disease relapse. Artificial intelligence and its subtypes, deep learning in particular, tend nowadays to have an expanding role in all fields of medicine, and diagnosing colon cancer is no exception. This report aims to summarize the entire application spectrum of deep learning in all diagnostic tests regarding colon cancer, from endoscopy and histologic examination to medical imaging and screening serologic tests.
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Affiliation(s)
- Dimitrios Bousis
- Department of Internal Medicine, General University Hospital of Patras, Patras, Greece
| | | | | | - Andreas Antzoulas
- Department of Surgery, General University Hospital of Patras, Patras, Greece
| | | | | | - Dimitrios Kehagias
- Department of Surgery, General University Hospital of Patras, Patras, Greece
| | | | - Konstantinos Kotis
- Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece
| | | | - Francesk Mulita
- Department of Surgery, General University Hospital of Patras, Patras, Greece
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Lei II, Tompkins K, White E, Watson A, Parsons N, Noufaily A, Segui S, Wenzek H, Badreldin R, Conlin A, Arasaradnam RP. Study of capsule endoscopy delivery at scale through enhanced artificial intelligence-enabled analysis (the CESCAIL study). Colorectal Dis 2023. [PMID: 37272471 DOI: 10.1111/codi.16575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 03/05/2023] [Accepted: 03/21/2023] [Indexed: 06/06/2023]
Abstract
AIM Lower gastrointestinal (GI) diagnostics have been facing relentless capacity constraints for many years, even before the COVID-19 era. Restrictions from the COVID pandemic have resulted in a significant backlog in lower GI diagnostics. Given recent developments in deep neural networks (DNNs) and the application of artificial intelligence (AI) in endoscopy, automating capsule video analysis is now within reach. Comparable to the efficiency and accuracy of AI applications in small bowel capsule endoscopy, AI in colon capsule analysis will also improve the efficiency of video reading and address the relentless demand on lower GI services. The aim of the CESCAIL study is to determine the feasibility, accuracy and productivity of AI-enabled analysis tools (AiSPEED) for polyp detection compared with the 'gold standard': a conventional care pathway with clinician analysis. METHOD This multi-centre, diagnostic accuracy study aims to recruit 674 participants retrospectively and prospectively from centres conducting colon capsule endoscopy (CCE) as part of their standard care pathway. After the study participants have undergone CCE, the colon capsule videos will be uploaded onto two different pathways: AI-enabled video analysis and the gold standard conventional clinician analysis pathway. The reports generated from both pathways will be compared for accuracy (sensitivity and specificity). The reading time can only be compared in the prospective cohort. In addition to validating the AI tool, this study will also provide observational data concerning its use to assess the pathway execution in real-world performance. RESULTS The study is currently recruiting participants at multiple centres within the United Kingdom and is at the stage of collecting data. CONCLUSION This standard diagnostic accuracy study carries no additional risk to patients as it does not affect the standard care pathway, and hence patient care remains unaffected.
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Affiliation(s)
- Ian Io Lei
- Department of Gastroenterology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Katie Tompkins
- Department of Gastroenterology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | | | - Angus Watson
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | | | | | - Santi Segui
- Department of Maths and Computer Science, University of Barcelona, Barcelona, Spain
| | - Hagen Wenzek
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Rawya Badreldin
- Department of Gastroenterology, James Paget University Hospitals NHS Foundation Trust, Lowestoft, UK
| | - Abby Conlin
- Department of Gastroenterology, Northern Care Alliance NHS Foundation Trust, Salford, UK
| | - Ramesh P Arasaradnam
- Department of Gastroenterology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Leicester Cancer Centre, University of Leicester, Leicester, UK
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18
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Mascarenhas M, Afonso J, Ribeiro T, Andrade P, Cardoso H, Macedo G. The Promise of Artificial Intelligence in Digestive Healthcare and the Bioethics Challenges It Presents. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59040790. [PMID: 37109748 PMCID: PMC10145124 DOI: 10.3390/medicina59040790] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/27/2023] [Accepted: 04/02/2023] [Indexed: 04/29/2023]
Abstract
With modern society well entrenched in the digital area, the use of Artificial Intelligence (AI) to extract useful information from big data has become more commonplace in our daily lives than we perhaps realize. Medical specialties that rely heavily on imaging techniques have become a strong focus for the incorporation of AI tools to aid disease diagnosis and monitoring, yet AI-based tools that can be employed in the clinic are only now beginning to become a reality. However, the potential introduction of these applications raises a number of ethical issues that must be addressed before they can be implemented, among the most important of which are issues related to privacy, data protection, data bias, explainability and responsibility. In this short review, we aim to highlight some of the most important bioethical issues that will have to be addressed if AI solutions are to be successfully incorporated into healthcare protocols, and ideally, before they are put in place. In particular, we contemplate the use of these aids in the field of gastroenterology, focusing particularly on capsule endoscopy and highlighting efforts aimed at resolving the issues associated with their use when available.
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Affiliation(s)
- Miguel Mascarenhas
- Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - Patrícia Andrade
- Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - Hélder Cardoso
- Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - Guilherme Macedo
- Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
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Aliyi S, Dese K, Raj H. Detection of Gastrointestinal Tract Disorders Using Deep Learning Methods from Colonoscopy Images and Videos. SCIENTIFIC AFRICAN 2023. [DOI: 10.1016/j.sciaf.2023.e01628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023] Open
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Galati JS, Duve RJ, O'Mara M, Gross SA. Artificial intelligence in gastroenterology: A narrative review. Artif Intell Gastroenterol 2022; 3:117-141. [DOI: 10.35712/aig.v3.i5.117] [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: 10/09/2022] [Revised: 11/21/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Medicine, NYU Langone Health, New York, NY 10016, United States
| | - Robert J Duve
- Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, United States
| | - Matthew O'Mara
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
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Saraiva MM, Ribeiro T, Afonso J, Boas FV, Ferreira JPS, Pereira P, Macedo G. Response. Gastrointest Endosc 2022; 96:1093-1094. [PMID: 36404093 DOI: 10.1016/j.gie.2022.08.007] [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: 07/31/2022] [Accepted: 08/07/2022] [Indexed: 11/19/2022]
Affiliation(s)
- Miguel Mascarenhas Saraiva
- Department of Gastroenterology, São João University HospitalPorto, Portugal; WGO Gastroenterology and Hepatology Training CenterPorto, Portugal; Faculty of Medicine, University of PortoPorto, Portugal
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University HospitalPorto, Portugal; WGO Gastroenterology and Hepatology Training CenterPorto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University HospitalPorto, Portugal; WGO Gastroenterology and Hepatology Training CenterPorto, Portugal
| | - Filipe Vilas Boas
- Department of Gastroenterology, São João University HospitalPorto, Portugal; WGO Gastroenterology and Hepatology Training CenterPorto, Portugal; Faculty of Medicine, University of Porto, Porto, Portugal
| | - João P S Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of PortoPorto, Portugal; Institute of Science and Innovation in Mechanical and Industrial EngineeringPorto, Portugal
| | - Pedro Pereira
- Department of Gastroenterology, São João University HospitalPorto, Portugal; WGO Gastroenterology and Hepatology Training CenterPorto, Portugal; Faculty of Medicine, University of Porto, Porto, Portugal
| | - Guilherme Macedo
- Department of Gastroenterology, São João University HospitalPorto, Portugal; WGO Gastroenterology and Hepatology Training CenterPorto, Portugal; Faculty of Medicine, University of Porto, Porto, Portugal
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Gilabert P, Vitrià J, Laiz P, Malagelada C, Watson A, Wenzek H, Segui S. Artificial intelligence to improve polyp detection and screening time in colon capsule endoscopy. Front Med (Lausanne) 2022; 9:1000726. [PMCID: PMC9606587 DOI: 10.3389/fmed.2022.1000726] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 09/23/2022] [Indexed: 11/13/2022] Open
Abstract
Colon Capsule Endoscopy (CCE) is a minimally invasive procedure which is increasingly being used as an alternative to conventional colonoscopy. Videos recorded by the capsule cameras are long and require one or more experts' time to review and identify polyps or other potential intestinal problems that can lead to major health issues. We developed and tested a multi-platform web application, AI-Tool, which embeds a Convolution Neural Network (CNN) to help CCE reviewers. With the help of artificial intelligence, AI-Tool is able to detect images with high probability of containing a polyp and prioritize them during the reviewing process. With the collaboration of 3 experts that reviewed 18 videos, we compared the classical linear review method using RAPID Reader Software v9.0 and the new software we present. Applying the new strategy, reviewing time was reduced by a factor of 6 and polyp detection sensitivity was increased from 81.08 to 87.80%.
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Affiliation(s)
- Pere Gilabert
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain,*Correspondence: Pere Gilabert
| | - Jordi Vitrià
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Pablo Laiz
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Carolina Malagelada
- Digestive System Research Unit, University Hospital Vall d'Hebron, Barcelona, Spain,Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Angus Watson
- Department of Colorectal Surgery, Raigmore Hospital, NHS Highland, Inverness, United Kingdom
| | - Hagen Wenzek
- CorporateHealth International ApS, Odense, Denmark
| | - Santi Segui
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
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