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Savino A, Rondonotti E, Rocchetto S, Piagnani A, Bina N, Di Domenico P, Segatta F, Radaelli F. GI genius endoscopy module: a clinical profile. Expert Rev Med Devices 2024; 21:359-372. [PMID: 38618982 DOI: 10.1080/17434440.2024.2342508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 04/09/2024] [Indexed: 04/16/2024]
Abstract
INTRODUCTION The identification of early-stage colorectal cancers (CRC) and the resection of pre-cancerous neoplastic lesions through colonoscopy allows to decrease both CRC incidence and mortality. However, colonoscopy miss rates up to 26% for adenomas and 9% for advanced adenomas have been reported. In recent years, artificial intelligence (AI) systems have been emerging as easy-to-use tools, potentially lowering the risk of missing lesions. AREAS COVERED This review paper focuses on GI Genius device (Medtronic Co. Minneapolis, MN, U.S.A.) a computer-assisted tool designed to assist endoscopists during standard white-light colonoscopies in detecting mucosal lesions. EXPERT OPINION Randomized controlled trials (RCTs) suggest that GI Genius is a safe and effective tool for improving adenoma detection, especially in CRC screening and surveillance colonoscopies. However, its impact seems to be less significant among experienced endoscopists and in real-world clinical scenarios compared to the controlled conditions of RCTs. Furthermore, it appears that GI Genius mainly enhances the detection of non-advanced, small polyps, but does not significantly impact the identification of advanced and difficult-to-detect adenoma. When using GI Genius, no complications were documented. Only a small number of studies reported an increased in withdrawal time or the removal of non-neoplastic lesions.
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Affiliation(s)
- Alberto Savino
- Division of Gastroenterology, Department of Medicine and Surgery, University of Milano-Bicocca, Milano, Italy
| | | | - Simone Rocchetto
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Gastroenterology and Hepatology, University of Milan, Milan, Italy
| | - Alessandra Piagnani
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Gastroenterology and Hepatology, University of Milan, Milan, Italy
| | - Niccolò Bina
- Gastroenterology Unit, Valduce Hospital, Como, Italy
| | - Pasquale Di Domenico
- Gastrointestinal Unit, Department of Medicine, Surgery & Dentistry Scuola Medica Salernitana, University of Salerno, Salerno, Italy
| | - Francesco Segatta
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Gastroenterology and Hepatology, University of Milan, Milan, Italy
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Jin XF, Ma HY, Shi JW, Cai JT. Efficacy of artificial intelligence in reducing miss rates of GI adenomas, polyps, and sessile serrated lesions: a meta-analysis of randomized controlled trials. Gastrointest Endosc 2024; 99:667-675.e1. [PMID: 38184117 DOI: 10.1016/j.gie.2024.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 11/20/2023] [Accepted: 01/01/2024] [Indexed: 01/08/2024]
Abstract
BACKGROUND AND AIMS The aim of this study was to determine if utilization of artificial intelligence (AI) in the course of endoscopic procedures can significantly diminish both the adenoma miss rate (AMR) and the polyp miss rate (PMR) compared with standard endoscopy. METHODS We performed an extensive search of various databases, encompassing PubMed, Embase, Cochrane Library, Web of Science, and Scopus, until June 2023. The search terms used were artificial intelligence, machine learning, deep learning, transfer machine learning, computer-assisted diagnosis, convolutional neural networks, gastrointestinal (GI) endoscopy, endoscopic image analysis, polyp, adenoma, and neoplasms. The main study aim was to explore the impact of AI on the AMR, PMR, and sessile serrated lesion miss rate. RESULTS A total of 7 randomized controlled trials were included in this meta-analysis. Pooled AMR was markedly lower in the AI group versus the non-AI group (pooled relative risk [RR], .46; 95% confidence interval [CI], .36-.59; P < .001). PMR was also reduced in the AI group in contrast with the non-AI control (pooled RR, .43; 95% CI, .27-.69; P < .001). The results showed that AI decreased the miss rate of sessile serrated lesions (pooled RR, .43; 95% CI, .20 to .92; P < .05) and diminutive adenomas (pooled RR, .49; 95% CI, .26-.93) during endoscopy, but no significant effect was observed for advanced adenomas (pooled RR, .48; 95% CI, .17-1.37; P = .17). The average number of polyps (Hedges' g = -.486; 95% CI, -.697 to -.274; P = .000) and adenomas (Hedges' g = -.312; 95% CI, -.551 to -.074; P = .01) detected during the second procedure also favored AI. However, AI implementation did not lead to a prolonged withdrawal time (P > .05). CONCLUSIONS This meta-analysis suggests that AI technology leads to significant reduction of miss rates for GI adenomas, polyps, and sessile serrated lesions during endoscopic surveillance. These results underscore the potential of AI to improve the accuracy and efficiency of GI endoscopic procedures.
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Affiliation(s)
- Xi-Feng Jin
- Department of Gastroenterology, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China.
| | - Hong-Yan Ma
- Tengzhou Central People's Hospital, Shandong Province, Zaozhuang, China
| | - Jun-Wen Shi
- Tengzhou Central People's Hospital, Shandong Province, Zaozhuang, China
| | - Jian-Ting Cai
- Department of Gastroenterology, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
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Patel HK, Mori Y, Hassan C, Rizkala T, Radadiya DK, Nathani P, Srinivasan S, Misawa M, Maselli R, Antonelli G, Spadaccini M, Facciorusso A, Khalaf K, Lanza D, Bonanno G, Rex DK, Repici A, Sharma P. Lack of Effectiveness of Computer Aided Detection for Colorectal Neoplasia: A Systematic Review and Meta-Analysis of Nonrandomized Studies. Clin Gastroenterol Hepatol 2024; 22:971-980.e15. [PMID: 38056803 DOI: 10.1016/j.cgh.2023.11.029] [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: 09/28/2023] [Revised: 11/19/2023] [Accepted: 11/22/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUND AND AIMS Benefits of computer-aided detection (CADe) in detecting colorectal neoplasia were shown in many randomized trials in which endoscopists' behavior was strictly controlled. However, the effect of CADe on endoscopists' performance in less-controlled setting is unclear. This systematic review and meta-analyses were aimed at clarifying benefits and harms of using CADe in real-world colonoscopy. METHODS We searched MEDLINE, EMBASE, Cochrane, and Google Scholar from inception to August 20, 2023. We included nonrandomized studies that compared the effectiveness between CADe-assisted and standard colonoscopy. Two investigators independently extracted study data and quality. Pairwise meta-analysis was performed utilizing risk ratio for dichotomous variables and mean difference (MD) for continuous variables with a 95% confidence interval (CI). RESULTS Eight studies were included, comprising 9782 patients (4569 with CADe and 5213 without CADe). Regarding benefits, there was a difference in neither adenoma detection rate (44% vs 38%; risk ratio, 1.11; 95% CI, 0.97 to 1.28) nor mean adenomas per colonoscopy (0.93 vs 0.79; MD, 0.14; 95% CI, -0.04 to 0.32) between CADe-assisted and standard colonoscopy, respectively. Regarding harms, there was no difference in the mean non-neoplastic lesions per colonoscopy (8 studies included for analysis; 0.52 vs 0.47; MD, 0.14; 95% CI, -0.07 to 0.34) and withdrawal time (6 studies included for analysis; 14.3 vs 13.4 minutes; MD, 0.8 minutes; 95% CI, -0.18 to 1.90). There was a substantial heterogeneity, and all outcomes were graded with a very low certainty of evidence. CONCLUSION CADe in colonoscopies neither improves the detection of colorectal neoplasia nor increases burden of colonoscopy in real-world, nonrandomized studies, questioning the generalizability of the results of randomized trials.
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Affiliation(s)
- Harsh K Patel
- Gastroenterology and Hepatology, University of Kansas Medical Center, Kansas City, Missouri
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway; Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, IRCCS Humanitas Clinical and Research Center, Rozzano, Italy.
| | - Tommy Rizkala
- Endoscopy Unit, IRCCS Humanitas Clinical and Research Center, Rozzano, Italy
| | - Dhruvil K Radadiya
- Gastroenterology and Hepatology, University of Kansas Medical Center, Kansas City, Missouri
| | - Piyush Nathani
- Gastroenterology and Hepatology, University of Kansas Medical Center, Kansas City, Missouri
| | - Sachin Srinivasan
- Gastroenterology and Hepatology, University of Kansas Medical Center, Kansas City, Missouri
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Roberta Maselli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, IRCCS Humanitas Clinical and Research Center, Rozzano, Italy
| | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli, Ariccia, Italy; Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, Sapienza University of Rome, Rome, Italy
| | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, IRCCS Humanitas Clinical and Research Center, Rozzano, Italy
| | - Antonio Facciorusso
- Section of Gastroenterology, Department of Medical Sciences, University of Foggia, Foggia, Italy
| | - Kareem Khalaf
- Division of Gastroenterology, St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Davide Lanza
- Gastroenterology and Hepatology, Clinica Moncucco, Lugano, Switzerland
| | - Giacomo Bonanno
- Endoscopy Unit, Humanitas Istituto Clinico Catanese, Catania, Italy
| | - Douglas K Rex
- Division of Gastroenterology, Indiana University School of Medicine, Indianapolis, Indiana
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, IRCCS Humanitas Clinical and Research Center, Rozzano, Italy
| | - Prateek Sharma
- Division of Gastroenterology, Indiana University School of Medicine, Indianapolis, Indiana; Gastroenterology and Hepatology, Kansas City VA Medical Center and University of Kansas School of Medicine, Kansas City, Missouri
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Leśniewska M, Patryn R, Kopystecka A, Kozioł I, Budzyńska J. Third Eye? The Assistance of Artificial Intelligence (AI) in the Endoscopy of Gastrointestinal Neoplasms. J Clin Med 2023; 12:6721. [PMID: 37959187 PMCID: PMC10650785 DOI: 10.3390/jcm12216721] [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: 09/04/2023] [Revised: 10/19/2023] [Accepted: 10/21/2023] [Indexed: 11/15/2023] Open
Abstract
Gastrointestinal cancers are characterized by high incidence and mortality. However, there are well-established methods of screening. The endoscopy exam provides the macroscopical image and enables harvesting the tissue samples for further histopathological diagnosis. The efficiency of endoscopies relies not only on proper patient preparation, but also on the skills of the personnel conducting the exam. In recent years, a number of reports concerning the application of artificial intelligence (AI) in medicine have arisen. Numerous studies aimed to assess the utility of deep learning/ neural network systems supporting endoscopies. In this review, we summarized the most recent reports and randomized clinical trials regarding the application of AI in screening and surveillance of gastrointestinal cancers among patients suffering from esophageal, gastric, and colorectal cancer, along with the advantages, limitations, and controversies of those novel solutions.
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Affiliation(s)
- Magdalena Leśniewska
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
| | - Rafał Patryn
- Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland
| | - Agnieszka Kopystecka
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
| | - Ilona Kozioł
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
| | - Julia Budzyńska
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
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Baumer S, Streicher K, Alqahtani SA, Brookman-Amissah D, Brunner M, Federle C, Muehlenberg K, Pfeifer L, Salzberger A, Schorr W, Zustin J, Pech O. Accuracy of polyp characterization by artificial intelligence and endoscopists: a prospective, non-randomized study in a tertiary endoscopy center. Endosc Int Open 2023; 11:E818-E828. [PMID: 37727511 PMCID: PMC10506867 DOI: 10.1055/a-2096-2960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/08/2023] [Indexed: 09/21/2023] Open
Abstract
Background and study aims Artificial intelligence (AI) in gastrointestinal endoscopy is developing very fast. Computer-aided detection of polyps and computer-aided diagnosis (CADx) for polyp characterization are available now. This study was performed to evaluate the diagnostic performance of a new commercially available CADx system in clinical practice. Patients and methods This prospective, non-randomized study was performed at a tertiary academic endoscopy center from March to August 2022. We included patients receiving a colonoscopy. Polypectomy had to be performed in all polyps. Every patient was examined concurrently by an endoscopist and AI using two opposing screens. The AI system, overseen by a second observer, was not visible to the endoscopist. The primary outcome was accuracy of the AI classifying the polyps into "neoplastic" and "non-neoplastic." The secondary outcome was accuracy of the classification by the endoscopists. Sessile serrated lesions were classified as neoplastic. Results We included 156 patients (mean age 65; 57 women) with 262 polyps ≤10 mm. Eighty-four were hyperplastic polyps (32.1%), 158 adenomas (60.3%), seven sessile serrated lesions (2.7%) and 13 other entities (normal/inflammatory colonmucosa, lymphoidic polyp) (4.9%) on histological diagnosis. Sensitivity, specificity and accuracy of AI were 89.70% (95% confidence interval [CI]: 84.02%-93.88%), 75.26% (95% CI: 65.46%-83.46%) and 84.35% (95% CI:79.38%-88.53%), respectively. Sensitivity, specificity and accuracy for less experienced endoscopists (2-5 years of endoscopy) were 95.56% (95% CI: 84.85%-99.46%), 61.54% (95% CI: 40.57%-79.77%) and 83.10% (95% CI: 72.34%-90.95%) and for experienced endoscopists 90.83% (95% CI: 84.19%-95.33%), 71.83% (95% CI: 59.90%-81.87%) and 83.77% (95% CI: 77.76%-88.70%), respectively. Conclusion Accuracy for polyp characterization by a new commercially available AI system is high, but does not fulfill the criteria for a "resect-and-discard" strategy.
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Affiliation(s)
- Sebastian Baumer
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Kilian Streicher
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Saleh A. Alqahtani
- Department of Gastroenterology and Hepatology, Johns Hopkins Hospital, Baltimore, United States
- Liver Transplant Center, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Dominic Brookman-Amissah
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Monika Brunner
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Christoph Federle
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Klaus Muehlenberg
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Lukas Pfeifer
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Andrea Salzberger
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Wolfgang Schorr
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Jozef Zustin
- Private Practice, Histopathology Service Private Practice, Regensburg, Germany
- Gerhard-Domagk-Institute of Pathology, Universitätsklinikum Münster, Munster, Germany
| | - Oliver Pech
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
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Engelke C, Graf M, Maass C, Tews HC, Kraus M, Ewers T, Sayk F, Solbach P, Zimpel C, Tharun L, Marquardt JU, Kirstein MM. Prospective study of computer-aided detection of colorectal adenomas in hospitalized patients. Scand J Gastroenterol 2023; 58:1194-1199. [PMID: 37191195 DOI: 10.1080/00365521.2023.2212309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 05/04/2023] [Indexed: 05/17/2023]
Abstract
BACKGROUND Adenoma detection with polypectomy during total colonoscopy reduces the incidence of colorectal cancer (CRC) and colorectal cancer-associated mortality. The adenoma detection rate (ADR) is an established quality indicator, which is associated with a decreased risk for interval cancer. An increase in ADR could be demonstrated for several artificially intelligent, real-time computer-aided detection (CADe) systems in selected patients. Most studies concentrated on outpatient colonoscopies. This sector often lacks funds for applying costly innovations like CADe. Hospitals are more likely to implement CADe and information about the impact of CADe in the distinct patient cohort of hospitalized patients is scarce. METHODS In this prospective, randomized-controlled study, we compared colonoscopies performed with or without computer-aided detection (CADe) system (GI Genius, Medtronic) performed at University Medical Center Schleswig-Holstein, Campus Luebeck. The primary endpoint was ADR. RESULTS Overall, 232 patients were randomized with n = 122 patients in the CADe arm and n = 110 patients in the control arm. Median age was 66 years (interquartile range 51-77). Indication for colonoscopy was most often workup for gastrointestinal symptoms (88.4%) followed by screening, post-polypectomy and post-CRC surveillance (each 3.9%). Withdrawal time was significantly prolonged (11 vs. 10 min, p = 0.039), without clinical relevance. Complication rate was not different between the arms (0.8% vs. 4.5%; p = 0.072). The ADR was significantly increased in the CADe arm compared to the control (33.6% vs. 18.1%, p = 0.008). ADR increase was particularly strong for the detection in elderly patients aged ≥50 years (OR 6.3, 95% CI 1.7 - 23.1, p = 0.006). CONCLUSION The use of CADe is safe and increases ADR in hospitalized patients.
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Affiliation(s)
- Carsten Engelke
- 1st Department of Medicine, University Medical Center Schleswig-Holstein, Luebeck, Germany
| | - Mattis Graf
- 1st Department of Medicine, University Medical Center Schleswig-Holstein, Luebeck, Germany
| | - Carlos Maass
- 1st Department of Medicine, University Medical Center Schleswig-Holstein, Luebeck, Germany
| | - Hauke C Tews
- 1st Department of Medicine, University Medical Center Schleswig-Holstein, Luebeck, Germany
- Department of Internal Medicine I, Gastroenterology, Hepatology, Endocrinology, Rheumatology and Infectious Diseases, University Hospital Regensburg, Regensburg, Germany
| | - Martin Kraus
- 1st Department of Medicine, University Medical Center Schleswig-Holstein, Luebeck, Germany
| | - Thomas Ewers
- 1st Department of Medicine, University Medical Center Schleswig-Holstein, Luebeck, Germany
| | - Friedhelm Sayk
- 1st Department of Medicine, University Medical Center Schleswig-Holstein, Luebeck, Germany
| | - Philipp Solbach
- 1st Department of Medicine, University Medical Center Schleswig-Holstein, Luebeck, Germany
| | - Carolin Zimpel
- 1st Department of Medicine, University Medical Center Schleswig-Holstein, Luebeck, Germany
| | - Lars Tharun
- Institute of Pathology, University Medical Center Schleswig-Holstein, Luebeck, Germany
| | - Jens U Marquardt
- 1st Department of Medicine, University Medical Center Schleswig-Holstein, Luebeck, Germany
| | - Martha M Kirstein
- 1st Department of Medicine, University Medical Center Schleswig-Holstein, Luebeck, Germany
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Galati JS, Lin K, Gross SA. Recent advances in devices and technologies that might prove revolutionary for colonoscopy procedures. Expert Rev Med Devices 2023; 20:1087-1103. [PMID: 37934873 DOI: 10.1080/17434440.2023.2280773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 11/03/2023] [Indexed: 11/09/2023]
Abstract
INTRODUCTION Colorectal cancer (CRC) is the third most common malignancy and second leading cause of cancer-related mortality in the world. Adenoma detection rate (ADR), a quality indicator for colonoscopy, has gained prominence as it is inversely related to CRC incidence and mortality. As such, recent efforts have focused on developing novel colonoscopy devices and technologies to improve ADR. AREAS COVERED The main objective of this paper is to provide an overview of advancements in the fields of colonoscopy mechanical attachments, artificial intelligence-assisted colonoscopy, and colonoscopy optical enhancements with respect to ADR. We accomplished this by performing a comprehensive search of multiple electronic databases from inception to September 2023. This review is intended to be an introduction to colonoscopy devices and technologies. EXPERT OPINION Numerous mechanical attachments and optical enhancements have been developed that have the potential to improve ADR and AI has gone from being an inaccessible concept to a feasible means for improving ADR. While these advances are exciting and portend a change in what will be considered standard colonoscopy, they continue to require refinement. Future studies should focus on combining modalities to further improve ADR and exploring the use of these technologies in other facets of colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Internal Medicine, NYU Langone Health, New York, NY, USA
| | - Kevin Lin
- Department of Internal Medicine, NYU Langone Health, New York, NY, USA
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY, USA
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Nehme F, Coronel E, Barringer DA, Romero LG, Shafi MA, Ross WA, Ge PS. Performance and attitudes toward real-time computer-aided polyp detection during colonoscopy in a large tertiary referral center in the United States. Gastrointest Endosc 2023; 98:100-109.e6. [PMID: 36801459 DOI: 10.1016/j.gie.2023.02.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/05/2023] [Accepted: 02/10/2023] [Indexed: 02/23/2023]
Abstract
BACKGROUND AND AIMS Computer-aided detection (CADe) has been shown to improve polyp detection in clinical trials. Limited data exist on the impact, utilization, and attitudes toward artificial intelligence (AI)-assisted colonoscopy in daily clinical practice. We aimed to evaluate the effectiveness of the first U.S. Food and Drug Administration-approved CADe device for polyp detection in the United States and the attitudes toward its implementation. METHODS We performed a retrospective analysis of a prospectively maintained database of patients undergoing colonoscopy at a tertiary center in the United States before and after a real-time CADe system was made available. The decision to activate the CADe system was at the discretion of the endoscopist. An anonymous survey was circulated among endoscopy physicians and staff at the beginning and conclusion of the study period regarding their attitudes toward AI-assisted colonoscopy. RESULTS CADe was activated in 52.1% of cases. Compared with historical control subjects, there was no statistically significant difference in adenomas detected per colonoscopy (1.08 vs 1.04, P = .65), even after excluding diagnostic and therapeutic indications and cases where CADe was not activated (1.27 vs 1.17, P = .45). In addition, there was no statistically significant difference in adenoma detection rate (ADR), median procedure, and withdrawal times. Survey results demonstrated mixed attitudes toward AI-assisted colonoscopy, of which main concerns were high number of false-positive signals (82.4%), high level of distraction (58.8%), and impression it prolonged procedure time (47.1%). CONCLUSIONS CADe did not improve adenoma detection in daily practice among endoscopists with high baseline ADRs. Despite its availability, AI-assisted colonoscopy was only activated in half of the cases, and multiple concerns were raised by staff and endoscopists. Future studies will help elucidate the patients and endoscopists that would benefit most from AI-assisted colonoscopy.
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Affiliation(s)
- Fredy Nehme
- Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Emmanuel Coronel
- Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Denise A Barringer
- Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Laura G Romero
- Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mehnaz A Shafi
- Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - William A Ross
- Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Phillip S Ge
- Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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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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10
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Yuan XL, Zhou Y, Liu W, Luo Q, Zeng XH, Yi Z, Hu B. Artificial intelligence for diagnosing gastric lesions under white-light endoscopy. Surg Endosc 2022; 36:9444-9453. [PMID: 35879572 DOI: 10.1007/s00464-022-09420-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 06/24/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND The ability of endoscopists to identify gastric lesions is uneven. Even experienced endoscopists may miss or misdiagnose lesions due to heavy workload or fatigue or subtle changes in lesions under white-light endoscopy (WLE). This study aimed to develop an artificial intelligence (AI) system that could diagnose six common gastric lesions under WLE and to explore its role in assisting endoscopists in diagnosis. METHODS Images of early gastric cancer, advanced gastric cancer, submucosal tumor, polyp, peptic ulcer, erosion, and lesion-free gastric mucosa were retrospectively collected to train and test the system. The performance of the system was compared with that of 12 endoscopists. The performance of endoscopists with or without referring to the system was also evaluated. RESULTS A total of 29,809 images from 8947 patients and 1579 images from 496 patients were used to train and test the system, respectively. For per-lesion analysis, the overall accuracy of the system was 85.7%, which was comparable to that of senior endoscopists (85.1%, P = 0.729) and significantly higher than that of junior endoscopists (78.8%, P < 0.001). With system assistance, the overall accuracies of senior and junior endoscopists increased to 89.3% (4.2%, P < 0.001) and 86.2% (7.4%, P < 0.001), respectively. Senior and junior endoscopists achieved varying degrees of improvement in the diagnostic performance of other types of lesions except for polyp. The diagnostic times of senior (3.8 vs 3.2 s per image, P = 0.500) and junior endoscopists (6.2 vs 4.6 s per image, P = 0.144) assisted by the system were both slightly shortened, despite no significant differences. CONCLUSIONS The proposed AI system could be applied as an auxiliary tool to reduce the workload of endoscopists and improve the diagnostic accuracy of gastric lesions.
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Affiliation(s)
- Xiang-Lei Yuan
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, Sichuan, China
| | - Yao Zhou
- Center of Intelligent Medicine, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road Chengdu, Chengdu, 610065, Sichuan, China
| | - Wei Liu
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, Sichuan, China
| | - Qi Luo
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, Sichuan, China
| | - Xian-Hui Zeng
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, Sichuan, China
| | - Zhang Yi
- Center of Intelligent Medicine, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road Chengdu, Chengdu, 610065, Sichuan, China.
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, Sichuan, China.
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Park J, Hwang Y, Kim HG, Lee JS, Kim JO, Lee TH, Jeon SR, Hong SJ, Ko BM, Kim S. Reduced detection rate of artificial intelligence in images obtained from untrained endoscope models and improvement using domain adaptation algorithm. Front Med (Lausanne) 2022; 9:1036974. [PMID: 36438041 PMCID: PMC9684642 DOI: 10.3389/fmed.2022.1036974] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/27/2022] [Indexed: 11/11/2022] Open
Abstract
A training dataset that is limited to a specific endoscope model can overfit artificial intelligence (AI) to its unique image characteristics. The performance of the AI may degrade in images of different endoscope model. The domain adaptation algorithm, i.e., the cycle-consistent adversarial network (cycleGAN), can transform the image characteristics into AI-friendly styles. We attempted to confirm the performance degradation of AIs in images of various endoscope models and aimed to improve them using cycleGAN transformation. Two AI models were developed from data of esophagogastroduodenoscopies collected retrospectively over 5 years: one for identifying the endoscope models, Olympus CV-260SL, CV-290 (Olympus, Tokyo, Japan), and PENTAX EPK-i (PENTAX Medical, Tokyo, Japan), and the other for recognizing the esophagogastric junction (EGJ). The AIs were trained using 45,683 standardized images from 1,498 cases and validated on 624 separate cases. Between the two endoscope manufacturers, there was a difference in image characteristics that could be distinguished without error by AI. The accuracy of the AI in recognizing gastroesophageal junction was >0.979 in the same endoscope-examined validation dataset as the training dataset. However, they deteriorated in datasets from different endoscopes. Cycle-consistent adversarial network can successfully convert image characteristics to ameliorate the AI performance. The improvements were statistically significant and greater in datasets from different endoscope manufacturers [original → AI-trained style, increased area under the receiver operating characteristic (ROC) curve, P-value: CV-260SL → CV-290, 0.0056, P = 0.0106; CV-260SL → EPK-i, 0.0182, P = 0.0158; CV-290 → CV-260SL, 0.0134, P < 0.0001; CV-290 → EPK-i, 0.0299, P = 0.0001; EPK-i → CV-260SL, 0.0215, P = 0.0024; and EPK-i → CV-290, 0.0616, P < 0.0001]. In conclusion, cycleGAN can transform the diverse image characteristics of endoscope models into an AI-trained style to improve the detection performance of AI.
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Affiliation(s)
- Junseok Park
- Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, South Korea
| | - Youngbae Hwang
- Department of Intelligent Systems and Robotics, Chungbuk National University, Cheongju, South Korea
| | - Hyun Gun Kim
- Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, South Korea
- *Correspondence: Hyun Gun Kim
| | - Joon Seong Lee
- Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, South Korea
| | - Jin-Oh Kim
- Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, South Korea
| | - Tae Hee Lee
- Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, South Korea
| | - Seong Ran Jeon
- Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, South Korea
| | - Su Jin Hong
- Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, South Korea
| | - Bong Min Ko
- Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, South Korea
| | - Seokmin Kim
- Department of Intelligent Systems and Robotics, Chungbuk National University, Cheongju, South Korea
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12
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Mori Y, Kaminski MF, Hassan C, Bretthauer M. Clinical trial designs for artificial intelligence in gastrointestinal endoscopy. Lancet Gastroenterol Hepatol 2022; 7:785-786. [PMID: 35932767 DOI: 10.1016/s2468-1253(22)00232-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 06/27/2022] [Indexed: 06/15/2023]
Affiliation(s)
- Yuichi Mori
- Clinical Effectiveness Research Group, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway; Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.
| | - Michal F Kaminski
- Clinical Effectiveness Research Group, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway; Department of Gastroenterology, Hepatology and Clinical Oncology, Centre of Postgraduate Medical Education, Warsaw, Poland; Department of Oncological Gastroenterology and Department of Cancer Prevention, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Cesare Hassan
- Endoscopy Unit, Humanitas Clinical and Research Center, Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Michael Bretthauer
- Clinical Effectiveness Research Group, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
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Rath T. Missed lesions and artificial intelligence during colonoscopy: the tireless working expert in the room. Endoscopy 2022; 54:473-474. [PMID: 34905793 DOI: 10.1055/a-1669-8814] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Timo Rath
- Department of Medicine, Ludwig Demling Endoscopy Center of Excellence, University Hospital Erlangen, Friedrich-Alexander-University, Erlangen-Nuernberg, Germany
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14
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Effect of artificial intelligence-aided colonoscopy for adenoma and polyp detection: a meta-analysis of randomized clinical trials. Int J Colorectal Dis 2022; 37:495-506. [PMID: 34762157 DOI: 10.1007/s00384-021-04062-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/29/2021] [Indexed: 02/04/2023]
Abstract
BACKGROUND This meta-analysis aimed to determine whether artificial intelligence (AI) improves colonoscopy outcome metrics i.e. adenoma detection rate (ADR) and polyp detection rate (PDR). METHODS Two authors independently searched Web of Science, PubMed, Science Direct, and Cochrane Library to find all published research before July 2021 that has compared AI-aided colonoscopy with routine colonoscopy (RC) for detection of adenoma and polyp. RESULTS This meta-analysis included 10 RCTs with 6629 individuals in AI-aided (n = 3300) and routine (n = 3329) groups. The results showed that both ADR (RR, 1.43; P < 0.001) and PDR (RR, 1.44; P < 0.001) using AI-aided endoscopy were significantly greater when compared with RC. The adenomas detected per colonoscopy (APC) (WMD, 0.25; P = 0.009), polyps detected per colonoscopy (PPC) (WMD, 0.52; P < 0.001), and sessile serrated lesions detected per colonoscopy (SSLPC) (RR, 1.53; P < 0.001) were significantly higher in the AI-aided group compared with the RC group. Subgroup analysis based on size, location, and shape of adenomas and polyps demonstrated that, except for in the cecum and pedunculated adenomas or polyps, the AI-aided groups of the other subgroups are more advantageous. Withdrawal time was longer in the AI-aided group when biopsies were included, while withdrawal time excluding biopsy time showed no significant difference. CONCLUSIONS AI-aided polyp detection system significantly increases lesion detection rate. In addition, lesion detection by AI is hardly affected by factors such as size, location, and shape.
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15
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Chen Y, Qu L, Guo L. How can we improve our operation? Endoscopy 2022; 54:222. [PMID: 35086156 DOI: 10.1055/a-1690-6518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Yonghao Chen
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China.,West China School of Medicine, Sichuan University, Chengdu, China
| | - Lang Qu
- West China School of Medicine, Sichuan University, Chengdu, China
| | - Linjie Guo
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
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16
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Pech O, Zippelius C. Reply to Chen et al. Endoscopy 2022; 54:223. [PMID: 35086157 DOI: 10.1055/a-1722-2963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Affiliation(s)
- Oliver Pech
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Carolin Zippelius
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
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