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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]
Abstract
BACKGROUNDS AND AIMS Mucosal healing (MH) in inflammatory bowel diseases (IBD) is an important landmark for clinical decision making. Artificial intelligence systems (AI) that automatically deliver the grade of endoscopic inflammation may solve moderate interobserver agreement and the need of central reading in clinical trials. METHODS We performed a systematic review of EMBASE and MEDLINE databases up to 01/12/2022 following PRISMA and the Joanna Briggs Institute methodologies to answer the following question: "Can AI replace endoscopists when assessing MH in IBD?". The research was restricted to ulcerative colitis (UC), and a diagnostic odds ratio (DOR) meta-analysis was performed. Risk of bias was evaluated with QUADAS-2 tool. RESULTS A total of 21 / 739 records were selected for full text evaluation, and 12 were included in the meta-analysis. Deep learning algorithms based on convolutional neural networks architecture achieved a satisfactory performance in evaluating MH on UC, with sensitivity, specificity, DOR and SROC of respectively 0.91(CI95 %:0.86-0.95);0.89(CI95 %:0.84-0.93);92.42(CI95 %:54.22-157.53) and 0.957 when evaluating fixed images (n = 8) and 0.86(CI95 %:0.75-0.93);0.91(CI95 %:0.87-0.94);70.86(CI95 %:24.63-203.86) and 0.941 when evaluating videos (n = 6). Moderate-high levels of heterogeneity were noted, limiting the quality of the evidence. CONCLUSIONS AI systems showed high potential in detecting MH in UC with optimal diagnostic performance, although moderate-high heterogeneity of the data was noted. Standardised and shared AI training may reduce heterogeneity between systems.
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Affiliation(s)
- Alessandro Rimondi
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, United Kingdom.
| | | | - Edward J Despott
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, United Kingdom
| | - Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Alberto Murino
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, United Kingdom; Department of Gastroenterology, Cleveland Clinic London, London, United Kingdom
| | - Gian Eugenio Tontini
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Gastroenterology and Endoscopy unit, Milan, Italy
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2
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Li M, Xiong X, Xu B, Dickson C. Chinese Oncologists' Perspectives on Integrating AI into Clinical Practice: Cross-Sectional Survey Study. JMIR Form Res 2024; 8:e53918. [PMID: 38838307 PMCID: PMC11187515 DOI: 10.2196/53918] [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: 10/24/2023] [Revised: 02/21/2024] [Accepted: 04/03/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND The rapid development of artificial intelligence (AI) has brought significant interest to its potential applications in oncology. Although AI-powered tools are already being implemented in some Chinese hospitals, their integration into clinical practice raises several concerns for Chinese oncologists. OBJECTIVE This study aims to explore the concerns of Chinese oncologists regarding the integration of AI into clinical practice and to identify the factors influencing these concerns. METHODS A total of 228 Chinese oncologists participated in a cross-sectional web-based survey from April to June in 2023 in mainland China. The survey gauged their worries about AI with multiple-choice questions. The survey evaluated their views on the statements of "The impact of AI on the doctor-patient relationship" and "AI will replace doctors." The data were analyzed using descriptive statistics, and variate analyses were used to find correlations between the oncologists' backgrounds and their concerns. RESULTS The study revealed that the most prominent concerns were the potential for AI to mislead diagnosis and treatment (163/228, 71.5%); an overreliance on AI (162/228, 71%); data and algorithm bias (123/228, 54%); issues with data security and patient privacy (123/228, 54%); and a lag in the adaptation of laws, regulations, and policies in keeping up with AI's development (115/228, 50.4%). Oncologists with a bachelor's degree expressed heightened concerns related to data and algorithm bias (34/49, 69%; P=.03) and the lagging nature of legal, regulatory, and policy issues (32/49, 65%; P=.046). Regarding AI's impact on doctor-patient relationships, 53.1% (121/228) saw a positive impact, whereas 35.5% (81/228) found it difficult to judge, 9.2% (21/228) feared increased disputes, and 2.2% (5/228) believed that there is no impact. Although sex differences were not significant (P=.08), perceptions varied-male oncologists tended to be more positive than female oncologists (74/135, 54.8% vs 47/93, 50%). Oncologists with a bachelor's degree (26/49, 53%; P=.03) and experienced clinicians (≥21 years; 28/56, 50%; P=.054). found it the hardest to judge. Those with IT experience were significantly more positive (25/35, 71%) than those without (96/193, 49.7%; P=.02). Opinions regarding the possibility of AI replacing doctors were diverse, with 23.2% (53/228) strongly disagreeing, 14% (32/228) disagreeing, 29.8% (68/228) being neutral, 16.2% (37/228) agreeing, and 16.7% (38/228) strongly agreeing. There were no significant correlations with demographic and professional factors (all P>.05). CONCLUSIONS Addressing oncologists' concerns about AI requires collaborative efforts from policy makers, developers, health care professionals, and legal experts. Emphasizing transparency, human-centered design, bias mitigation, and education about AI's potential and limitations is crucial. Through close collaboration and a multidisciplinary strategy, AI can be effectively integrated into oncology, balancing benefits with ethical considerations and enhancing patient care.
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Affiliation(s)
- Ming Li
- Department of Health Policy Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - XiaoMin Xiong
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing University School of Medicine, Chongqing, China
| | - Bo Xu
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing University School of Medicine, Chongqing, China
- Department of Biochemistry and Molecular Biology, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Cancer Research Center, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Conan Dickson
- Department of Health Policy Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
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Mota J, Almeida MJ, Mendes F, Martins M, Ribeiro T, Afonso J, Cardoso P, Cardoso H, Andrade P, Ferreira J, Mascarenhas M, Macedo G. From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy? Diagnostics (Basel) 2024; 14:291. [PMID: 38337807 PMCID: PMC10855436 DOI: 10.3390/diagnostics14030291] [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: 11/13/2023] [Revised: 01/09/2024] [Accepted: 01/16/2024] [Indexed: 02/12/2024] Open
Abstract
The role of capsule endoscopy and enteroscopy in managing various small-bowel pathologies is well-established. However, their broader application has been hampered mainly by their lengthy reading times. As a result, there is a growing interest in employing artificial intelligence (AI) in these diagnostic and therapeutic procedures, driven by the prospect of overcoming some major limitations and enhancing healthcare efficiency, while maintaining high accuracy levels. In the past two decades, the applicability of AI to gastroenterology has been increasing, mainly because of the strong imaging component. Nowadays, there are a multitude of studies using AI, specifically using convolutional neural networks, that prove the potential applications of AI to these endoscopic techniques, achieving remarkable results. These findings suggest that there is ample opportunity for AI to expand its presence in the management of gastroenterology diseases and, in the future, catalyze a game-changing transformation in clinical activities. This review provides an overview of the current state-of-the-art of AI in the scope of small-bowel study, with a particular focus on capsule endoscopy and enteroscopy.
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Affiliation(s)
- Joana Mota
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Maria João Almeida
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Helder Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Patrícia Andrade
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal;
- Digestive Artificial Intelligence Development, R. Alfredo Allen 455-461, 4200-135 Porto, Portugal
| | - Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- ManopH Gastroenterology Clinic, R. de Sá da Bandeira 752, 4000-432 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
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Brodersen JB, Jensen MD, Leenhardt R, Kjeldsen J, Histace A, Knudsen T, Dray X. Artificial Intelligence-assisted Analysis of Pan-enteric Capsule Endoscopy in Patients with Suspected Crohn's Disease: A Study on Diagnostic Performance. J Crohns Colitis 2024; 18:75-81. [PMID: 37527554 DOI: 10.1093/ecco-jcc/jjad131] [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: 05/30/2023] [Indexed: 08/03/2023]
Abstract
BACKGROUND AND AIM Pan-enteric capsule endoscopy [PCE] is a highly sensitive but time-consuming tool for detecting pathology. Artificial intelligence [AI] algorithms might offer a possibility to assist in the review and reduce the analysis time of PCE. This study examines the agreement between PCE assessments aided by AI technology and standard evaluations, in patients suspected of Crohn's disease [CD]. METHOD PCEs from a prospective, blinded, multicentre study, including patients suspected of CD, were processed by the deep learning solution AXARO® [Augmented Endoscopy, Paris, France]. Based on the image output, two observers classified the patient's PCE as normal or suggestive of CD, ulcerative colitis, or cancer. The primary outcome was per-patient sensitivities and specificities for detecting CD and inflammatory bowel disease [IBD]. Complete reading of PCE served as the reference standard. RESULTS A total of 131 patients' PCEs were analysed, with a median recording time of 303 min. The AXARO® framework reduced output to a median of 470 images [2.1%] per patient, and the pooled median review time was 3.2 min per patient. For detecting CD, the observers had a sensitivity of 96% and 92% and a specificity of 93% and 90%, respectively. For the detection of IBD, both observers had a sensitivity of 97% and had a specificity of 91% and 90%, respectively. The negative predictive value was 95% for CD and 97% for IBD. CONCLUSIONS Using the AXARO® framework reduced the initial review time substantially while maintaining high diagnostic accuracy-suggesting its use as a rapid tool to rule out IBD in PCEs of patients suspected of Crohn's disease.
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Affiliation(s)
- Jacob Broder Brodersen
- Department of Internal Medicine, Section of Gastroenterology, Hospital of South West Jutland, Esbjerg, Denmark
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Michael Dam Jensen
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
- Department of Internal Medicine, Section of Gastroenterology, Lillebaelt Hospital, Vejle, Denmark
| | - Romain Leenhardt
- Équipes Traitement de l'Information et Systèmes, ETIS UMR 8051, CY Paris Cergy University, ENSEA, CNRS, Cergy, France
- Sorbonne University, Center for Digestive Endoscopy, Saint-Antoine Hospital, APHP, Paris, France
| | - Jens Kjeldsen
- Department of Medical Gastroenterology, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- OPEN Odense Patient Data Explorative Network, Odense University Hospital, Odense, Denmark
| | - Aymeric Histace
- Équipes Traitement de l'Information et Systèmes, ETIS UMR 8051, CY Paris Cergy University, ENSEA, CNRS, Cergy, France
| | - Torben Knudsen
- Department of Internal Medicine, Section of Gastroenterology, Hospital of South West Jutland, Esbjerg, Denmark
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Xavier Dray
- Équipes Traitement de l'Information et Systèmes, ETIS UMR 8051, CY Paris Cergy University, ENSEA, CNRS, Cergy, France
- Sorbonne University, Center for Digestive Endoscopy, Saint-Antoine Hospital, APHP, Paris, France
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
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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: 1.5] [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
Abstract
BACKGROUND Artificial intelligence (AI) is rapidly infiltrating multiple areas in medicine, with gastrointestinal endoscopy paving the way in both research and clinical applications. Multiple challenges associated with the incorporation of AI in endoscopy are being addressed in recent consensus documents. OBJECTIVES In the current paper, we aimed to map future challenges and areas of research for the incorporation of AI in capsule endoscopy (CE) practice. DESIGN Modified three-round Delphi consensus online survey. METHODS The study design was based on a modified three-round Delphi consensus online survey distributed to a group of CE and AI experts. Round one aimed to map out key research statements and challenges for the implementation of AI in CE. All queries addressing the same questions were merged into a single issue. The second round aimed to rank all generated questions during round one and to identify the top-ranked statements with the highest total score. Finally, the third round aimed to redistribute and rescore the top-ranked statements. RESULTS Twenty-one (16 gastroenterologists and 5 data scientists) experts participated in the survey. In the first round, 48 statements divided into seven themes were generated. After scoring all statements and rescoring the top 12, the question of AI use for identification and grading of small bowel pathologies was scored the highest (mean score 9.15), correlation of AI and human expert reading-second (9.05), and real-life feasibility-third (9.0). CONCLUSION In summary, our current study points out a roadmap for future challenges and research areas on our way to fully incorporating AI in CE reading.
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Affiliation(s)
| | - 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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Chen M, Zhang B, Cai Z, Seery S, Gonzalez MJ, Ali NM, Ren R, Qiao Y, Xue P, Jiang Y. Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey. Front Med (Lausanne) 2022; 9:990604. [PMID: 36117979 PMCID: PMC9472134 DOI: 10.3389/fmed.2022.990604] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background Artificial intelligence (AI) needs to be accepted and understood by physicians and medical students, but few have systematically assessed their attitudes. We investigated clinical AI acceptance among physicians and medical students around the world to provide implementation guidance. Materials and methods We conducted a two-stage study, involving a foundational systematic review of physician and medical student acceptance of clinical AI. This enabled us to design a suitable web-based questionnaire which was then distributed among practitioners and trainees around the world. Results Sixty studies were included in this systematic review, and 758 respondents from 39 countries completed the online questionnaire. Five (62.50%) of eight studies reported 65% or higher awareness regarding the application of clinical AI. Although, only 10–30% had actually used AI and 26 (74.28%) of 35 studies suggested there was a lack of AI knowledge. Our questionnaire uncovered 38% awareness rate and 20% utility rate of clinical AI, although 53% lacked basic knowledge of clinical AI. Forty-five studies mentioned attitudes toward clinical AI, and over 60% from 38 (84.44%) studies were positive about AI, although they were also concerned about the potential for unpredictable, incorrect results. Seventy-seven percent were optimistic about the prospect of clinical AI. The support rate for the statement that AI could replace physicians ranged from 6 to 78% across 40 studies which mentioned this topic. Five studies recommended that efforts should be made to increase collaboration. Our questionnaire showed 68% disagreed that AI would become a surrogate physician, but believed it should assist in clinical decision-making. Participants with different identities, experience and from different countries hold similar but subtly different attitudes. Conclusion Most physicians and medical students appear aware of the increasing application of clinical AI, but lack practical experience and related knowledge. Overall, participants have positive but reserved attitudes about AI. In spite of the mixed opinions around clinical AI becoming a surrogate physician, there was a consensus that collaborations between the two should be strengthened. Further education should be conducted to alleviate anxieties associated with change and adopting new technologies.
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Affiliation(s)
- Mingyang Chen
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bo Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ziting Cai
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Samuel Seery
- Faculty of Health and Medicine, Division of Health Research, Lancaster University, Lancaster, United Kingdom
| | | | - Nasra M. Ali
- The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Ran Ren
- Global Health Research Center, Dalian Medical University, Dalian, China
| | - Youlin Qiao
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Youlin Qiao,
| | - Peng Xue
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Peng Xue,
| | - Yu Jiang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Yu Jiang,
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7
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Sahafi A, Wang Y, Rasmussen CLM, Bollen P, Baatrup G, Blanes-Vidal V, Herp J, Nadimi ES. Edge artificial intelligence wireless video capsule endoscopy. Sci Rep 2022; 12:13723. [PMID: 35962014 PMCID: PMC9374669 DOI: 10.1038/s41598-022-17502-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 07/26/2022] [Indexed: 11/09/2022] Open
Abstract
Gastrointestinal (GI) tract diseases are responsible for substantial morbidity and mortality worldwide, including colorectal cancer, which has shown a rising incidence among adults younger than 50. Although this could be alleviated by regular screening, only a small percentage of those at risk are screened comprehensively, due to shortcomings in accuracy and patient acceptance. To address these challenges, we designed an artificial intelligence (AI)-empowered wireless video endoscopic capsule that surpasses the performance of the existing solutions by featuring, among others: (1) real-time image processing using onboard deep neural networks (DNN), (2) enhanced visualization of the mucous layer by deploying both white-light and narrow-band imaging, (3) on-the-go task modification and DNN update using over-the-air-programming and (4) bi-directional communication with patient's personal electronic devices to report important findings. We tested our solution in an in vivo setting, by administrating our endoscopic capsule to a pig under general anesthesia. All novel features, successfully implemented on a single platform, were validated. Our study lays the groundwork for clinically implementing a new generation of endoscopic capsules, which will significantly improve early diagnosis of upper and lower GI tract diseases.
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Affiliation(s)
- A Sahafi
- Applied AI and Data Science (AID), Maersk Mc-Kinney Moller Institute, Faculty of Engineering, University of Southern Denmark, Odense, Denmark
| | - Y Wang
- Applied AI and Data Science (AID), Maersk Mc-Kinney Moller Institute, Faculty of Engineering, University of Southern Denmark, Odense, Denmark
| | - C L M Rasmussen
- Biomedical Laboratory, Department of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - P Bollen
- Biomedical Laboratory, Department of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - G Baatrup
- Department of Surgery, Odense University Hospital, Odense, Denmark.,Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - V Blanes-Vidal
- Applied AI and Data Science (AID), Maersk Mc-Kinney Moller Institute, Faculty of Engineering, University of Southern Denmark, Odense, Denmark.,Danish Center for Clinical Artificial Intelligence (CAI-X), Odense, Denmark
| | - J Herp
- Applied AI and Data Science (AID), Maersk Mc-Kinney Moller Institute, Faculty of Engineering, University of Southern Denmark, Odense, Denmark.,Danish Center for Clinical Artificial Intelligence (CAI-X), Odense, Denmark
| | - E S Nadimi
- Applied AI and Data Science (AID), Maersk Mc-Kinney Moller Institute, Faculty of Engineering, University of Southern Denmark, Odense, Denmark. .,Danish Center for Clinical Artificial Intelligence (CAI-X), Odense, Denmark.
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Son G, Eo T, An J, Oh DJ, Shin Y, Rha H, Kim YJ, Lim YJ, Hwang D. Small Bowel Detection for Wireless Capsule Endoscopy Using Convolutional Neural Networks with Temporal Filtering. Diagnostics (Basel) 2022; 12:diagnostics12081858. [PMID: 36010210 PMCID: PMC9406835 DOI: 10.3390/diagnostics12081858] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/28/2022] [Accepted: 07/28/2022] [Indexed: 12/22/2022] Open
Abstract
By automatically classifying the stomach, small bowel, and colon, the reading time of the wireless capsule endoscopy (WCE) can be reduced. In addition, it is an essential first preprocessing step to localize the small bowel in order to apply automated small bowel lesion detection algorithms based on deep learning. The purpose of the study was to develop an automated small bowel detection method from long untrimmed videos captured from WCE. Through this, the stomach and colon can also be distinguished. The proposed method is based on a convolutional neural network (CNN) with a temporal filtering on the predicted probabilities from the CNN. For CNN, we use a ResNet50 model to classify three organs including stomach, small bowel, and colon. The hybrid temporal filter consisting of a Savitzky–Golay filter and a median filter is applied to the temporal probabilities for the “small bowel” class. After filtering, the small bowel and the other two organs are differentiated with thresholding. The study was conducted on dataset of 200 patients (100 normal and 100 abnormal WCE cases), which was divided into a training set of 140 cases, a validation set of 20 cases, and a test set of 40 cases. For the test set of 40 patients (20 normal and 20 abnormal WCE cases), the proposed method showed accuracy of 99.8% in binary classification for the small bowel. Transition time errors for gastrointestinal tracts were only 38.8 ± 25.8 seconds for the transition between stomach and small bowel and 32.0 ± 19.1 seconds for the transition between small bowel and colon, compared to the ground truth organ transition points marked by two experienced gastroenterologists.
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Affiliation(s)
- Geonhui Son
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Taejoon Eo
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Jiwoong An
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Dong Jun Oh
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Korea;
| | - Yejee Shin
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Hyenogseop Rha
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - You Jin Kim
- IntroMedic, Capsule Endoscopy Medical Device Manufacturer, Seoul 08375, Korea;
| | - Yun Jeong Lim
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Korea;
- Correspondence: (Y.J.L.); (D.H.)
| | - Dosik Hwang
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
- Center for Healthcare Robotics, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, Korea
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul 03722, Korea
- Department of Radiology and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul 03722, Korea
- Correspondence: (Y.J.L.); (D.H.)
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Piccirelli S, Mussetto A, Bellumat A, Cannizzaro R, Pennazio M, Pezzoli A, Bizzotto A, Fusetti N, Valiante F, Hassan C, Pecere S, Koulaouzidis A, Spada C. New Generation Express View: An Artificial Intelligence Software Effectively Reduces Capsule Endoscopy Reading Times. Diagnostics (Basel) 2022; 12:diagnostics12081783. [PMID: 35892494 PMCID: PMC9332221 DOI: 10.3390/diagnostics12081783] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 07/15/2022] [Accepted: 07/21/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND: Reading capsule endoscopy (CE) is time-consuming. The Express View (EV) (IntroMedic, Seoul, Korea) software was designed to shorten CE video reading. Our primary aim was to evaluate the diagnostic accuracy of EV in detecting significant small-bowel (SB) lesions. We also compared the reading times with EV mode and standard reading (SR). METHODS: 126 patients with suspected SB bleeding and/or suspected neoplasia were prospectively enrolled and underwent SB CE (MiroCam®1200, IntroMedic, Seoul, Korea). CE evaluation was performed in standard and EV mode. In case of discrepancies between SR and EV readings, a consensus was reached after reviewing the video segments and the findings were re-classified. RESULTS: The completion rate of SB CE in our cohort was 86.5% and no retention occurred. The per-patient analysis of sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of EV compared to SR were 86%, 86%, 90%, 81%, and 86%, respectively, before consensus. After consensus, they increased to 97%, 100%, 100%, 96%, and 98%, respectively. The median reading time with SR and EV was 71 min (range 26−340) and 13 min (range 3−85), respectively (p < 0.001). CONCLUSIONS: The new-generation EV shows high diagnostic accuracy and significantly reduces CE reading times.
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Affiliation(s)
- Stefania Piccirelli
- Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (S.P.); (A.B.); (C.S.)
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | | | | | | | - Marco Pennazio
- Division of Gastroenterology, University City of Health and Science University Hospital, 10121 Turin, Italy;
| | - Alessandro Pezzoli
- Endoscopy Unit, Department of Gastroenterology, Sant’Anna University Hospital, 44121 Ferrara, Italy; (A.P.); (N.F.)
| | - Alessandra Bizzotto
- Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (S.P.); (A.B.); (C.S.)
| | - Nadia Fusetti
- Endoscopy Unit, Department of Gastroenterology, Sant’Anna University Hospital, 44121 Ferrara, Italy; (A.P.); (N.F.)
| | | | - Cesare Hassan
- Endoscopy Unit, Department of Gastroenterology, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy;
| | - Silvia Pecere
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
- Correspondence:
| | - Anastasios Koulaouzidis
- Department of Medicine, Odense University Hospital Svendborg Sygehus, 5700 Svendborg, Denmark;
- Department of Clinical Research, University of Southern Denmark (SDU), 5230 Odense, Denmark
- Surgical Research Unit, Odense University Hospital, 5000 Odense, Denmark
- Department of Social Medicine and Public Health, Pomeranian Medical University, 70-204 Szczecin, Poland
| | - Cristiano Spada
- Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (S.P.); (A.B.); (C.S.)
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
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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: 0] [Impact Index Per Article: 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
Abstract
(1) Background: Villous atrophy is an indication for small bowel capsule endoscopy (SBCE). However, SBCE findings are not described uniformly and atrophic features are sometimes not recognized; (2) Methods: The Delphi technique was employed to reach agreement among a panel of SBCE experts. The nomenclature and definitions of SBCE lesions suggesting the presence of atrophy were decided in a core group of 10 experts. Four images of each lesion were chosen from a large SBCE database and agreement on the correspondence between the picture and the definition was evaluated using the Delphi method in a broadened group of 36 experts. All images corresponded to histologically proven mucosal atrophy; (3) Results: Four types of atrophic lesions were identified: mosaicism, scalloping, folds reduction, and granular mucosa. The core group succeeded in reaching agreement on the nomenclature and the descriptions of these items. Consensus in matching the agreed definitions for the proposed set of images was met for mosaicism (88.9% in the first round), scalloping (97.2% in the first round), and folds reduction (94.4% in the first round), but granular mucosa failed to achieve consensus (75.0% in the third round); (4) Conclusions: Consensus among SBCE experts on atrophic lesions was met for the first time. Mosaicism, scalloping, and folds reduction are the most reliable signs, while the description of granular mucosa remains uncertain.
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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.)
| | | | - 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.)
| | | | - 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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