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Spadaccini M, Troya J, Khalaf K, Facciorusso A, Maselli R, Hann A, Repici A. Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going? Dig Liver Dis 2024; 56:1148-1155. [PMID: 38458884 DOI: 10.1016/j.dld.2024.01.203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 03/10/2024]
Abstract
Colorectal cancer is a significant global health concern, necessitating effective screening strategies to reduce its incidence and mortality rates. Colonoscopy plays a crucial role in the detection and removal of colorectal neoplastic precursors. However, there are limitations and variations in the performance of endoscopists, leading to missed lesions and suboptimal outcomes. The emergence of artificial intelligence (AI) in endoscopy offers promising opportunities to improve the quality and efficacy of screening colonoscopies. In particular, AI applications, including computer-aided detection (CADe) and computer-aided characterization (CADx), have demonstrated the potential to enhance adenoma detection and optical diagnosis accuracy. Additionally, AI-assisted quality control systems aim to standardize the endoscopic examination process. This narrative review provides an overview of AI principles and discusses the current knowledge on AI-assisted endoscopy in the context of screening colonoscopies. It highlights the significant role of AI in improving lesion detection, characterization, and quality assurance during colonoscopy. However, further well-designed studies are needed to validate the clinical impact and cost-effectiveness of AI-assisted colonoscopy before its widespread implementation.
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Affiliation(s)
- Marco Spadaccini
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy.
| | - Joel Troya
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Kareem Khalaf
- Division of Gastroenterology, St. Michael's Hospital, University of Toronto, Toronto, Canada
| | - Antonio Facciorusso
- Gastroenterology Unit, Department of Surgical and Medical Sciences, University of Foggia, Foggia, Italy
| | - Roberta Maselli
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
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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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Gangwani MK, Haghbin H, Ishtiaq R, Hasan F, Dillard J, Jaber F, Dahiya DS, Ali H, Salim S, Lee-Smith W, Sohail AH, Inamdar S, Aziz M, Hart B. Single Versus Second Observer vs Artificial Intelligence to Increase the ADENOMA Detection Rate of Colonoscopy-A Network Analysis. Dig Dis Sci 2024; 69:1380-1388. [PMID: 38436866 PMCID: PMC11026252 DOI: 10.1007/s10620-024-08341-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 02/07/2024] [Indexed: 03/05/2024]
Abstract
BACKGROUND AND AIMS Screening colonoscopy has significantly contributed to the reduction of the incidence of colorectal cancer (CRC) and its associated mortality, with adenoma detection rate (ADR) as the quality marker. To increase the ADR, various solutions have been proposed including the utilization of Artificial Intelligence (AI) and employing second observers during colonoscopies. In the interest of AI improving ADR independently, without a second observer, and the operational similarity between AI and second observer, this network meta-analysis aims at evaluating the effectiveness of AI, second observer, and a single observer in improving ADR. METHODS We searched the Medline, Embase, Cochrane, Web of Science Core Collection, Korean Citation Index, SciELO, Global Index Medicus, and Cochrane. A direct head-to-head comparator analysis and network meta-analysis were performed using the random-effects model. The odds ratio (OR) was calculated with a 95% confidence interval (CI) and p-value < 0.05 was considered statistically significant. RESULTS We analyzed 26 studies, involving 22,560 subjects. In the direct comparative analysis, AI demonstrated higher ADR (OR: 0.668, 95% CI 0.595-0.749, p < 0.001) than single observer. Dual observer demonstrated a higher ADR (OR: 0.771, 95% CI 0.688-0.865, p < 0.001) than single operator. In network meta-analysis, results were consistent on the network meta-analysis, maintaining consistency. No statistical difference was noted when comparing AI to second observer. (RR 1.1 (0.9-1.2, p = 0.3). Results were consistent when evaluating only RCTs. Net ranking provided higher score to AI followed by second observer followed by single observer. CONCLUSION Artificial Intelligence and second-observer colonoscopy showed superior success in Adenoma Detection Rate when compared to single-observer colonoscopy. Although not statistically significant, net ranking model favors the superiority of AI to the second observer.
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Affiliation(s)
| | - Hossein Haghbin
- Department of Gastroenterology and Hepatology, Ascension Providence Hospital, Southfield, MI, USA
| | - Rizwan Ishtiaq
- Department of Medicine, St Francis Hospital and Medical Center, Hartford, CT, USA
| | - Fariha Hasan
- Department of Internal Medicine, Cooper University Hospital, Camden, NJ, USA
| | - Julia Dillard
- Department of Medicine, University of Toledo Medical Center, Toledo, OH, USA
| | - Fouad Jaber
- Department of Internal Medicine, University of Missouri-Kansas City, Kansas City, MO, USA
| | - Dushyant Singh Dahiya
- Department of Medicine, Central Michigan University College of Medicine, Mount Pleasant, MI, USA
| | - Hassam Ali
- Department of Gastroenterology and Hepatology, East Carolina University Health, Greenville, NC, USA
| | - Shaharyar Salim
- Department of Internal Medicine, Aga Khan University Hospital, Karachi, Pakistan
| | - Wade Lee-Smith
- University of Toledo Libraries, University of Toledo, Toledo, OH, USA
| | - Amir Humza Sohail
- Department of General Surgery, New York University Langone Health, Long Island, NY, USA
| | - Sumant Inamdar
- Department of Gastroenterology and Hepatology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Muhammad Aziz
- Department of Gastroenterology and Hepatology, University of Toledo Medical Center, Toledo, OH, USA
| | - Benjamin Hart
- Depertment of Hepatology and Gastroenterology, University of Michigan, Ann Arbor, MI, USA
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Lei X, Dai J, Qiu D, Peng L, Weng X, Xia M, Luo X. The effect of nurse assisted colonoscopy on adenoma detection rates: A meta-analysis of randomized controlled trials. Int J Colorectal Dis 2024; 39:19. [PMID: 38227195 DOI: 10.1007/s00384-023-04585-5] [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] [Accepted: 12/21/2023] [Indexed: 01/17/2024]
Abstract
BACKGROUND Adenoma's detection rates have been reported to vary with the participation status of endoscopic nurses during colonoscopy. This meta-analysis was conducted to determine whether the participation of endoscopy nurses during colonoscopy contributed to the improved detection rate of polyps and adenomas. METHODS We retrieved English original research from PubMed, Embase, Web of Science, and Cochrane library databases and Chinese original research from the CNKI Data database. We searched for randomized controlled trials (RCTs) comparing the effect of participation of endoscopy nurses during colonoscopy of colorectal polyps and adenomas on polyp detection rates to that of nonparticipation. RevMan5.4 software was used to perform the meta-analysis. RESULTS This meta-analysis included 11 randomized controlled trials involving 8278 patients. The results showed no significant difference between colonoscopies performed by nurses and endoscopists, but colonoscopies performed by two nurses significantly improved the detection rate of polyps and adenomas. In the random effects model, there was a significant difference in PDR between the single-observation and dual-observation groups (RR, 1.27; 95%CI, 1.05, 1.54; Z = 2.51; P = 0.01). The ADR difference between the single observation group and the double observation group was statistically significant (RR, 1.15; 95%CI, 1.05, 1.26; Z = 2.91; P = 0.004). CONCLUSION Endoscopy nurses' participation in colonoscopy can improve the detection rate of polyps and adenomas, However, more research is needed to confirm the results.
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Affiliation(s)
- Xiaoju Lei
- Center for General Practice Medicine, Department of Endoscopy Center, Department of Nursing, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), 158 Shangtang Road, Hangzhou, 310014, Zhejiang, China
- Center for General Practice Medicine, Department of Nursing, Zhejiang Provincial People's Hospital Bijie Hospital, Bijie, Guizhou, China
| | - Jing Dai
- Center for General Practice Medicine, Department of Nursing, Zhejiang Provincial People's Hospital Bijie Hospital, Bijie, Guizhou, China
- Center for General Practice Medicine, Department of Nursing, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), 158 Shangtang Road, Hangzhou, 310014, Zhejiang, China
| | - Danying Qiu
- Center for General Practice Medicine, Department of Endoscopy Center, Department of Nursing, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), 158 Shangtang Road, Hangzhou, 310014, Zhejiang, China
| | - Liping Peng
- Center for General Practice Medicine, Department of Nursing, Zhejiang Provincial People's Hospital Bijie Hospital, Bijie, Guizhou, China
- Center for General Practice Medicine, Department of Nursing, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), 158 Shangtang Road, Hangzhou, 310014, Zhejiang, China
| | - Xiuping Weng
- Center for General Practice Medicine, Department of Nursing, Zhejiang Provincial People's Hospital Bijie Hospital, Bijie, Guizhou, China
- Center for General Practice Medicine, Department of Nursing, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), 158 Shangtang Road, Hangzhou, 310014, Zhejiang, China
| | - Meidan Xia
- Center for General Practice Medicine, Department of Nursing, Zhejiang Provincial People's Hospital Bijie Hospital, Bijie, Guizhou, China
- Center for General Practice Medicine, Department of Nursing, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), 158 Shangtang Road, Hangzhou, 310014, Zhejiang, China
| | - Xiaoting Luo
- Center for General Practice Medicine, Department of Nursing, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), 158 Shangtang Road, Hangzhou, 310014, Zhejiang, China.
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Xin Y, Zhang Q, Liu X, Li B, Mao T, Li X. Application of artificial intelligence in endoscopic gastrointestinal tumors. Front Oncol 2023; 13:1239788. [PMID: 38144533 PMCID: PMC10747923 DOI: 10.3389/fonc.2023.1239788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 11/17/2023] [Indexed: 12/26/2023] Open
Abstract
With an increasing number of patients with gastrointestinal cancer, effective and accurate early diagnostic clinical tools are required provide better health care for patients with gastrointestinal cancer. Recent studies have shown that artificial intelligence (AI) plays an important role in the diagnosis and treatment of patients with gastrointestinal tumors, which not only improves the efficiency of early tumor screening, but also significantly improves the survival rate of patients after treatment. With the aid of efficient learning and judgment abilities of AI, endoscopists can improve the accuracy of diagnosis and treatment through endoscopy and avoid incorrect descriptions or judgments of gastrointestinal lesions. The present article provides an overview of the application status of various artificial intelligence in gastric and colorectal cancers in recent years, and the direction of future research and clinical practice is clarified from a clinical perspective to provide a comprehensive theoretical basis for AI as a promising diagnostic and therapeutic tool for gastrointestinal cancer.
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Affiliation(s)
| | | | | | | | | | - Xiaoyu Li
- Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China
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Arora A, McDonald C, Guizzetti L, Iansavichene A, Brahmania M, Khanna N, Wilson A, Jairath V, Sey M. Endoscopy Unit Level Interventions to Improve Adenoma Detection Rate: A Systematic Review and Meta-Analysis. Clin Gastroenterol Hepatol 2023; 21:3238-3257. [PMID: 37080261 DOI: 10.1016/j.cgh.2023.03.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 03/22/2023] [Accepted: 03/29/2023] [Indexed: 04/22/2023]
Abstract
BACKGROUND & AIMS Adenoma detection rate (ADR) is inversely correlated with the risk of interval colon cancer and is a key target for quality improvement in endoscopy units. We conducted a systematic review and meta-analysis to identify and evaluate the effectiveness of interventions that can be implemented at the endoscopy unit level to improve ADRs. METHODS Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic search was conducted in MEDLINE, Embase, and Cochrane Central Register of Controlled Trials databases between January 1990 and December 2022 to identify relevant studies. Both randomized controlled trials and observational studies were eligible. Data for the primary outcome of ADR were analyzed and reported on the log-odds scale with 95% CIs using a random-effects meta-analysis model using the empiric Bayes estimator. RESULTS From 10,778 initial citations, 34 studies were included in the meta-analysis comprising 371,041 procedures and 1501 endoscopists. The provision of report cards (odds ratio [OR], 1.28; 95% CI, 1.13-1.45; P < .001) and the presence of an additional observer to identify polyps (OR, 1.25; 95% CI, 1.09-1.43; P = .002) were associated with significant increases in ADRs whereas multimodal interventions were borderline significant (OR, 1.18; 95% CI, 1.00-1.40; P = .05) and withdrawal time monitoring was not associated significantly with an increase in ADRs (OR, 1.35; 95% CI, 0.93-1.96; P = .11). CONCLUSIONS The provision of report cards and the presence of an additional observer to identify polyps are associated with improved ADRs and should be considered for implementation in endoscopy facilities.
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Affiliation(s)
- Anshul Arora
- Division of Gastroenterology, Western University, London, Ontario, Canada
| | - Cassandra McDonald
- Division of Gastroenterology, Western University, London, Ontario, Canada
| | | | - Alla Iansavichene
- Library Services, London Health Sciences Centre, London, Ontario, Canada
| | - Mayur Brahmania
- Division of Gastroenterology, Western University, London, Ontario, Canada; Lawson Health Research Institute, London Health Sciences Centre, London, Ontario, Canada
| | - Nitin Khanna
- Division of Gastroenterology, Western University, London, Ontario, Canada
| | - Aze Wilson
- Division of Gastroenterology, Western University, London, Ontario, Canada; Lawson Health Research Institute, London Health Sciences Centre, London, Ontario, Canada; Division of Clinical Pharmacology, Western University, London, Ontario, Canada; Department of Physiology and Pharmacology, Western University, London, Ontario, Canada
| | - Vipul Jairath
- Division of Gastroenterology, Western University, London, Ontario, Canada; Lawson Health Research Institute, London Health Sciences Centre, London, Ontario, Canada; Department of Epidemiology and Biostatistics, Western University, London, Ontario, Canada
| | - Michael Sey
- Division of Gastroenterology, Western University, London, Ontario, Canada; Lawson Health Research Institute, London Health Sciences Centre, London, Ontario, Canada.
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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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Sun X, Zhang Q, Wu S, Xu C, Zhang Y, Hao X, Meng Y, Jiao Y, Li H, Zhu S, Zhou Y, Liu K, Xu H, Zhu S, Zhang S. Effect of 3-Dimensional Imaging Device on Polyp and Adenoma Detection During Colonoscopy: A Randomized Controlled Trial. Am J Gastroenterol 2023; 118:1812-1820. [PMID: 37410933 DOI: 10.14309/ajg.0000000000002396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 06/13/2023] [Indexed: 07/08/2023]
Abstract
INTRODUCTION To evaluate the effect of 3-dimensional (3D) imaging device on polyp and adenoma detection during colonoscopy. METHODS In a single-blind, randomized controlled trial, participants aged 18-70 years who underwent diagnostic or screening colonoscopy were consecutively enrolled between August 2019 and May 2022. Each participant was randomized in a 1:1 ratio to undergo either 2-dimensional (2D-3D) colonoscopy or 3D-2D colonoscopy through computer-generated random numbers. Primary outcome included polyp detection rate (PDR) and adenoma detection rate (ADR), defined as the proportion of individuals with at least 1 polyp or adenoma detected during colonoscopy. The primary analysis was intention-to-treat. RESULTS Of 1,196 participants recruited, 571 in 2D-3D group and 583 in 3D-2D group were finally included after excluding those who met the exclusion criteria. The PDR between 2D and 3D groups was separately 39.6% and 40.5% during phase 1 (odds ratio [OR] = 0.96, 95% confidence interval [CI]: 0.76-1.22, P = 0.801), whereas PDR was significantly higher in 3D group (27.7%) than that of 2D group (19.9%) during phase 2, with a 1.54-fold increase (1.17-2.02, P = 0.002). Similarly, the ADR during phase 1 between 2D (24.7%) and 3D (23.8%) groups was not significant (OR = 1.05, 0.80-1.37, P = 0.788), while ADR was significantly higher in 3D group (13.8%) than that of 2D group (9.9%) during phase 2, with a 1.45-fold increase (1.01-2.08, P = 0.041). Further subgroup analysis confirmed significantly higher PDR and ADR of 3D group during phase 2, particularly in midlevel and junior endoscopists. DISCUSSION The 3D imaging device could improve overall PDR and ADR during colonoscopy, particularly in midlevel and junior endoscopists. Trial number: ChiCTR1900025000.
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Affiliation(s)
- Xiujing Sun
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Disease, Beijing Digestive Disease Center, Beijing Key Laboratory for Precancerous Lesion of Digestive Disease, Beijing, China
| | - Qian Zhang
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Disease, Beijing Digestive Disease Center, Beijing Key Laboratory for Precancerous Lesion of Digestive Disease, Beijing, China
| | - Shanshan Wu
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Disease, Beijing Digestive Disease Center, Beijing Key Laboratory for Precancerous Lesion of Digestive Disease, Beijing, China
| | - Changqin Xu
- Department of Gastroenterology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Yang Zhang
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Disease, Beijing Digestive Disease Center, Beijing Key Laboratory for Precancerous Lesion of Digestive Disease, Beijing, China
| | - Xiaowen Hao
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Disease, Beijing Digestive Disease Center, Beijing Key Laboratory for Precancerous Lesion of Digestive Disease, Beijing, China
| | - Ying Meng
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Disease, Beijing Digestive Disease Center, Beijing Key Laboratory for Precancerous Lesion of Digestive Disease, Beijing, China
| | - Yue Jiao
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Disease, Beijing Digestive Disease Center, Beijing Key Laboratory for Precancerous Lesion of Digestive Disease, Beijing, China
| | - Hongmei Li
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Disease, Beijing Digestive Disease Center, Beijing Key Laboratory for Precancerous Lesion of Digestive Disease, Beijing, China
| | - Siying Zhu
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Disease, Beijing Digestive Disease Center, Beijing Key Laboratory for Precancerous Lesion of Digestive Disease, Beijing, China
| | - Yanhua Zhou
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Disease, Beijing Digestive Disease Center, Beijing Key Laboratory for Precancerous Lesion of Digestive Disease, Beijing, China
| | - Kuiliang Liu
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Disease, Beijing Digestive Disease Center, Beijing Key Laboratory for Precancerous Lesion of Digestive Disease, Beijing, China
| | - Hongwei Xu
- Department of Gastroenterology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Shengtao Zhu
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Disease, Beijing Digestive Disease Center, Beijing Key Laboratory for Precancerous Lesion of Digestive Disease, Beijing, China
| | - Shutian Zhang
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Disease, Beijing Digestive Disease Center, Beijing Key Laboratory for Precancerous Lesion of Digestive Disease, Beijing, China
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Mansour NM. Artificial Intelligence in Colonoscopy. Curr Gastroenterol Rep 2023; 25:122-129. [PMID: 37129831 DOI: 10.1007/s11894-023-00872-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/03/2023] [Indexed: 05/03/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is a rapidly growing field in gastrointestinal endoscopy, and its potential applications are virtually endless, with studies demonstrating use of AI for early gastric cancer, inflammatory bowel disease, Barrett's esophagus, capsule endoscopy, as well as other areas in gastroenterology. Much of the early studies and applications of AI in gastroenterology have revolved around colonoscopy, particularly with regards to real-time polyp detection and characterization. This review will cover much of the existing data on computer-aided detection (CADe), computer-aided diagnosis (CADx), and briefly discuss some other interesting applications of AI for colonoscopy, while also considering some of the challenges and limitations that exist around the use of AI for colonoscopy. RECENT FINDINGS Multiple randomized controlled trials have now been published which show a statistically significant improvement when using AI to improve adenoma detection and reduce adenoma miss rates during colonoscopy. There is also a growing pool of literature showing that AI can be helpful for characterizing/diagnosing colorectal polyps in real time. AI has also shown promise in other areas of colonoscopy, including polyp sizing and automated measurement and monitoring of quality metrics during colonoscopy. AI is a promising tool that has the ability to shape the future of gastrointestinal endoscopy, with much of the early data showing significant benefits to use of AI during colonoscopy. However, there remain several challenges that may delay or hamper the widespread use of AI in the field.
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Affiliation(s)
- Nabil M Mansour
- Section of Gastroenterology and Hepatology, Baylor College of Medicine, 7200 Cambridge St., Suite 8B, Houston, TX, 77030, USA.
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Mazumdar S, Sinha S, Jha S, Jagtap B. Computer-aided automated diminutive colonic polyp detection in colonoscopy by using deep machine learning system; first indigenous algorithm developed in India. Indian J Gastroenterol 2023; 42:226-232. [PMID: 37145230 DOI: 10.1007/s12664-022-01331-7] [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: 03/11/2022] [Accepted: 12/18/2022] [Indexed: 05/06/2023]
Abstract
BACKGROUND Colonic polyps can be detected and resected during a colonoscopy before cancer development. However, about 1/4th of the polyps could be missed due to their small size, location or human errors. An artificial intelligence (AI) system can improve polyp detection and reduce colorectal cancer incidence. We are developing an indigenous AI system to detect diminutive polyps in real-life scenarios that can be compatible with any high-definition colonoscopy and endoscopic video- capture software. METHODS We trained a masked region-based convolutional neural network model to detect and localize colonic polyps. Three independent datasets of colonoscopy videos comprising 1,039 image frames were used and divided into a training dataset of 688 frames and a testing dataset of 351 frames. Of 1,039 image frames, 231 were from real-life colonoscopy videos from our centre. The rest were from publicly available image frames already modified to be directly utilizable for developing the AI system. The image frames of the testing dataset were also augmented by rotating and zooming the images to replicate real-life distortions of images seen during colonoscopy. The AI system was trained to localize the polyp by creating a 'bounding box'. It was then applied to the testing dataset to test its accuracy in detecting polyps automatically. RESULTS The AI system achieved a mean average precision (equivalent to specificity) of 88.63% for automatic polyp detection. All polyps in the testing were identified by AI, i.e., no false-negative result in the testing dataset (sensitivity of 100%). The mean polyp size in the study was 5 (± 4) mm. The mean processing time per image frame was 96.4 minutes. CONCLUSIONS This AI system, when applied to real-life colonoscopy images, having wide variations in bowel preparation and small polyp size, can detect colonic polyps with a high degree of accuracy.
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Affiliation(s)
- Srijan Mazumdar
- Indian Institute of Liver and Digestive Sciences, Sitala (East), Jagadishpur, Sonarpur, 24 Parganas (South), Kolkata, 700 150, India.
| | - Saugata Sinha
- Visvesvaraya National Institute of Technology, South Ambazari Road, Nagpur, 440 010, India
| | - Saurabh Jha
- Visvesvaraya National Institute of Technology, South Ambazari Road, Nagpur, 440 010, India
| | - Balaji Jagtap
- Visvesvaraya National Institute of Technology, South Ambazari Road, Nagpur, 440 010, India
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11
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Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening. Diagnostics (Basel) 2023; 13:diagnostics13061102. [PMID: 36980409 PMCID: PMC10047293 DOI: 10.3390/diagnostics13061102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/19/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023] Open
Abstract
Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic screening program is expected to strongly decrease colorectal cancer incidence and mortality. In this regard, quality of colonoscopy has been clearly related with the risk of post-colonoscopy colorectal cancer. Recently, the development of artificial intelligence (AI) applications in the medical field has been growing in interest. Through machine learning processes, and, more recently, deep learning, if a very high numbers of learning samples are available, AI systems may automatically extract specific features from endoscopic images/videos without human intervention, helping the endoscopists in different aspects of their daily practice. The aim of this review is to summarize the current knowledge on AI-aided endoscopy, and to outline its potential role in colorectal cancer prevention.
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The Role of an Artificial Intelligence Method of Improving the Diagnosis of Neoplasms by Colonoscopy. Diagnostics (Basel) 2023; 13:diagnostics13040701. [PMID: 36832189 PMCID: PMC9955100 DOI: 10.3390/diagnostics13040701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 01/30/2023] [Accepted: 02/06/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Colorectal cancer (CRC) is the third most common cancer worldwide. Colonoscopy is the gold standard examination that reduces the morbidity and mortality of CRC. Artificial intelligence (AI) could be useful in reducing the errors of the specialist and in drawing attention to the suspicious area. METHODS A prospective single-center randomized controlled study was conducted in an outpatient endoscopy unit with the aim of evaluating the usefulness of AI-assisted colonoscopy in PDR and ADR during the day time. It is important to understand how already available CADe systems improve the detection of polyps and adenomas in order to make a decision about their routine use in practice. In the period from October 2021 to February 2022, 400 examinations (patients) were included in the study. One hundred and ninety-four patients were examined using the ENDO-AID CADe artificial intelligence device (study group), and 206 patients were examined without the artificial intelligence (control group). RESULTS None of the analyzed indicators (PDR and ADR during morning and afternoon colonoscopies) showed differences between the study and control groups. There was an increase in PDR during afternoon colonoscopies, as well as ADR during morning and afternoon colonoscopies. CONCLUSIONS Based on our results, the use of AI systems in colonoscopies is recommended, especially in circumstances of an increase of examinations. Additional studies with larger groups of patients at night are needed to confirm the already available data.
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13
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Wang P, Liu XG, Kang M, Peng X, Shu ML, Zhou GY, Liu PX, Xiong F, Deng MM, Xia HF, Li JJ, Long XQ, Song Y, Li LP. Artificial intelligence empowers the second-observer strategy for colonoscopy: a randomized clinical trial. Gastroenterol Rep (Oxf) 2023; 11:goac081. [PMID: 36686571 PMCID: PMC9850273 DOI: 10.1093/gastro/goac081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 01/21/2023] Open
Abstract
Background In colonoscopy screening for colorectal cancer, human vision limitations may lead to higher miss rate of lesions; artificial intelligence (AI) assistance has been demonstrated to improve polyp detection. However, there still lacks direct evidence to demonstrate whether AI is superior to trainees or experienced nurses as a second observer to increase adenoma detection during colonoscopy. In this study, we aimed to compare the effectiveness of assistance from AI and human observer during colonoscopy. Methods A prospective multicenter randomized study was conducted from 2 September 2019 to 29 May 2020 at four endoscopy centers in China. Eligible patients were randomized to either computer-aided detection (CADe)-assisted group or observer-assisted group. The primary outcome was adenoma per colonoscopy (APC). Secondary outcomes included polyp per colonoscopy (PPC), adenoma detection rate (ADR), and polyp detection rate (PDR). We compared continuous variables and categorical variables by using R studio (version 3.4.4). Results A total of 1,261 (636 in the CADe-assisted group and 625 in the observer-assisted group) eligible patients were analysed. APC (0.42 vs 0.35, P = 0.034), PPC (1.13 vs 0.81, P < 0.001), PDR (47.5% vs 37.4%, P < 0.001), ADR (25.8% vs 24.0%, P = 0.464), the number of detected sessile polyps (683 vs 464, P < 0.001), and sessile adenomas (244 vs 182, P = 0.005) were significantly higher in the CADe-assisted group than in the observer-assisted group. False detections of the CADe system were lower than those of the human observer (122 vs 191, P < 0.001). Conclusions Compared with the human observer, the CADe system may improve the clinical outcome of colonoscopy and reduce disturbance to routine practice (Chictr.org.cn No.: ChiCTR1900025235).
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Affiliation(s)
| | | | - Min Kang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P. R. China
| | - Xue Peng
- Department of Gastroenterology, Xinqiao Hospital, Third Military Medical University, Chongqing, P. R. China
| | - Mei-Ling Shu
- Department of Gastroenterology, Suining Central Hospital, Suining, Sichuan, P. R. China
| | - Guan-Yu Zhou
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Pei-Xi Liu
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Fei Xiong
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Ming-Ming Deng
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P. R. China
| | - Hong-Fen Xia
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P. R. China
| | - Jian-Jun Li
- Department of Gastroenterology, Xinqiao Hospital, Third Military Medical University, Chongqing, P. R. China
| | - Xiao-Qi Long
- Department of Gastroenterology, Suining Central Hospital, Suining, Sichuan, P. R. China
| | - Yan Song
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Liang-Ping Li
- Corresponding author. Department of Gastroenterology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No.32 West Second Section, First Ring Road, Chengdu, Sichuan 610072, China. Tel: +86-28-8739 3927;
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14
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Nurse's Roles in Colorectal Cancer Prevention: A Narrative Review. JOURNAL OF PREVENTION (2022) 2022; 43:759-782. [PMID: 36001253 DOI: 10.1007/s10935-022-00694-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/21/2022] [Indexed: 02/07/2023]
Abstract
The objective of this paper is to investigate the different roles of nurses as members of healthcare teams at the primary, secondary, and tertiary levels of colorectal cancer prevention. The research team conducted a narrative review of studies involving the role of nurses at different levels of colorectal cancer prevention, which included a variety of quantitative, qualitative, and mixed-method studies. We searched PubMed, Scopus, Web of Science, Cochrane Reviews, Magiran, the Scientific Information Database (SID), Noormags, and the Islamic Science Citation (ISC) databases from ab initio until 2021. A total of 117 studies were reviewed. Nurses' roles were classified into three levels of prevention. At the primary level, the most important role related to educating people to prevent cancer and reduce risk factors. At the secondary level, the roles consisted of genetic counseling, stool testing, sigmoidoscopy and colonoscopy, biopsy and screening test follow-ups, and chemotherapy intervention, while at the tertiary level, their roles were made up of pre-and post-operative care to prevent further complications, rehabilitation, and palliative care. Nurses at various levels of prevention care also act as educators, coordinators, performers of screening tests, follow-up, and provision of palliative and end-of-life care. If these roles are not fulfilled at some levels of colorectal cancer, it is generally due to the lack of knowledge and competence of nurses or the lack of instruction and legal support for them. Nurses need sufficient clinical knowledge and experience to perform these roles at all levels.
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15
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Messmann H, Bisschops R, Antonelli G, Libânio D, Sinonquel P, Abdelrahim M, Ahmad OF, Areia M, Bergman JJGHM, Bhandari P, Boskoski I, Dekker E, Domagk D, Ebigbo A, Eelbode T, Eliakim R, Häfner M, Haidry RJ, Jover R, Kaminski MF, Kuvaev R, Mori Y, Palazzo M, Repici A, Rondonotti E, Rutter MD, Saito Y, Sharma P, Spada C, Spadaccini M, Veitch A, Gralnek IM, Hassan C, Dinis-Ribeiro M. Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy 2022; 54:1211-1231. [PMID: 36270318 DOI: 10.1055/a-1950-5694] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
This ESGE Position Statement defines the expected value of artificial intelligence (AI) for the diagnosis and management of gastrointestinal neoplasia within the framework of the performance measures already defined by ESGE. This is based on the clinical relevance of the expected task and the preliminary evidence regarding artificial intelligence in artificial or clinical settings. MAIN RECOMMENDATIONS:: (1) For acceptance of AI in assessment of completeness of upper GI endoscopy, the adequate level of mucosal inspection with AI should be comparable to that assessed by experienced endoscopists. (2) For acceptance of AI in assessment of completeness of upper GI endoscopy, automated recognition and photodocumentation of relevant anatomical landmarks should be obtained in ≥90% of the procedures. (3) For acceptance of AI in the detection of Barrett's high grade intraepithelial neoplasia or cancer, the AI-assisted detection rate for suspicious lesions for targeted biopsies should be comparable to that of experienced endoscopists with or without advanced imaging techniques. (4) For acceptance of AI in the management of Barrett's neoplasia, AI-assisted selection of lesions amenable to endoscopic resection should be comparable to that of experienced endoscopists. (5) For acceptance of AI in the diagnosis of gastric precancerous conditions, AI-assisted diagnosis of atrophy and intestinal metaplasia should be comparable to that provided by the established biopsy protocol, including the estimation of extent, and consequent allocation to the correct endoscopic surveillance interval. (6) For acceptance of artificial intelligence for automated lesion detection in small-bowel capsule endoscopy (SBCE), the performance of AI-assisted reading should be comparable to that of experienced endoscopists for lesion detection, without increasing but possibly reducing the reading time of the operator. (7) For acceptance of AI in the detection of colorectal polyps, the AI-assisted adenoma detection rate should be comparable to that of experienced endoscopists. (8) For acceptance of AI optical diagnosis (computer-aided diagnosis [CADx]) of diminutive polyps (≤5 mm), AI-assisted characterization should match performance standards for implementing resect-and-discard and diagnose-and-leave strategies. (9) For acceptance of AI in the management of polyps ≥ 6 mm, AI-assisted characterization should be comparable to that of experienced endoscopists in selecting lesions amenable to endoscopic resection.
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Affiliation(s)
- Helmut Messmann
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, Catholic University of Leuven (KUL), TARGID, University Hospital Leuven, Leuven, Belgium
| | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli Hospital, Ariccia, Rome, Italy
- Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, Sapienza University of Rome, Italy
| | - Diogo Libânio
- Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto, Portugal
- MEDCIDS, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Pieter Sinonquel
- Department of Gastroenterology and Hepatology, Catholic University of Leuven (KUL), TARGID, University Hospital Leuven, Leuven, Belgium
| | - Mohamed Abdelrahim
- Endoscopy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Omer F Ahmad
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London Hospital, London, UK
- Division of Surgery and Interventional Sciences, University College London Hospital, London, UK
- Gastrointestinal Services, University College London Hospital, London, UK
| | - Miguel Areia
- Gastroenterology Department, Portuguese Oncology Institute of Coimbra, Coimbra, Portugal
| | | | - Pradeep Bhandari
- Endoscopy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Ivo Boskoski
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam UMC, Amsterdam, The Netherlands
| | - Dirk Domagk
- Department of Medicine I, Josephs-Hospital Warendorf, Academic Teaching Hospital, University of Muenster, Warendorf, Germany
| | - Alanna Ebigbo
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Tom Eelbode
- Department of Electrical Engineering (ESAT/PSI), Medical Imaging Research Center, KU Leuven, Leuven, Belgium
| | - Rami Eliakim
- Department of Gastroenterology, Sheba Medical Center Tel Hashomer & Sackler School of Medicine, Tel-Aviv University, Ramat Gan, Israel
| | - Michael Häfner
- 2nd Medical Department, Barmherzige Schwestern Krankenhaus, Vienna, Austria
| | - Rehan J Haidry
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London Hospital, London, UK
- Division of Surgery and Interventional Sciences, University College London Hospital, London, UK
| | - Rodrigo Jover
- Servicio de Gastroenterología, Hospital General Universitario Dr. Balmis, Instituto de Investigación Biomédica de Alicante ISABIAL, Departamento de Medicina Clínica, Universidad Miguel Hernández, Alicante, Spain
| | - Michal F Kaminski
- Clinical Effectiveness Research Group, 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
| | - Roman Kuvaev
- Endoscopy Department, Yaroslavl Regional Cancer Hospital, Yaroslavl, Russian Federation
- Department of Gastroenterology, Faculty of Additional Professional Education, N.A. Pirogov Russian National Research Medical University, Moscow, Russian Federation
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | | | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | | | - Matthew D Rutter
- North Tees and Hartlepool NHS Foundation Trust, Stockton-on-Tees, UK
- Population Health Sciences Institute, Newcastle University, Newcastle, UK
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Prateek Sharma
- Gastroenterology and Hepatology Division, University of Kansas School of Medicine, Kansas, USA
- Kansas City VA Medical Center, Kansas City, USA
| | - Cristiano Spada
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Digestive Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Andrew Veitch
- Department of Gastroenterology, Royal Wolverhampton Hospitals NHS Trust, Wolverhampton, UK
| | - Ian M Gralnek
- Ellen and Pinchas Mamber Institute of Gastroenterology and Hepatology, Emek Medical Center, Afula, Israel
- Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Mario Dinis-Ribeiro
- Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto, Portugal
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Unno S, Igarashi K, Saito H, Hirasawa D, Okuzono T, Tanaka Y, Nakahori M, Matsuda T. Assigning a different endoscopist for each annual follow-up may contribute to improved gastric cancer detection rates. Endosc Int Open 2022; 10:E1333-E1342. [PMID: 36262509 PMCID: PMC9576325 DOI: 10.1055/a-1922-6429] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 08/03/2022] [Indexed: 12/03/2022] Open
Abstract
Background and study aims Esophagogastroduodenoscopy (EGD) is an effective and important diagnostic tool to detect gastric cancer (GC). Although previous studies show that examiner, patient, and instrumental factors influence the detection of GC, we analyzed whether assigning a different examiner to surveillance EGD would improve the detection of GC compared to assigning the same examiner as in the previous endoscopy. Patients and methods We retrospectively reviewed patients who underwent two or more consecutive surveillance EGDs at a single center between 2017 and 2019. We identified factors associated with GC detection using multivariable regression analysis and propensity-score matching. Results Among 7794 patients, 99 GC lesions in 93 patients were detected by surveillance EGD (detection rate; 1.2 %), with a mean surveillance interval of 11.2 months. Among the detected 99 lesions, 87 (87.9 %) were curatively treated with endoscopy. There were no differences in the clinicopathologic characteristics of GC detected by the same or different endoscopists. GC detection in the group examined by different endoscopists was more statistically significant than in the group examined by the same endoscopist, even after propensity-score matching (1.6 % and 0.7 %; P < 0.05). Endoscopic experience and other factors were not statistically significant between the two groups. Conclusions In surveillance EGD, having a different endoscopist for each exam may improve GC detection rates, regardless of the endoscopist's experience.
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Affiliation(s)
- Shuhei Unno
- Department of Gastroenterology, Sendai Kousei Hospital, Miyagi, Japan,Department of Gastroenterology, Seirei Hamamatsu General Hospital, Shizuoka, Japan
| | - Kimihiro Igarashi
- Department of Gastroenterology, Sendai Kousei Hospital, Miyagi, Japan
| | - Hiroaki Saito
- Department of Gastroenterology, Sendai Kousei Hospital, Miyagi, Japan
| | - Dai Hirasawa
- Department of Gastroenterology, Sendai Kousei Hospital, Miyagi, Japan
| | - Toru Okuzono
- Department of Gastroenterology, Sendai Kousei Hospital, Miyagi, Japan
| | - Yukari Tanaka
- Department of Gastroenterology, Sendai Kousei Hospital, Miyagi, Japan
| | - Masato Nakahori
- Department of Gastroenterology, Sendai Kousei Hospital, Miyagi, Japan
| | - Tomoki Matsuda
- Department of Gastroenterology, Sendai Kousei Hospital, Miyagi, Japan
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17
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Seager A, Sharp L, Hampton JS, Neilson LJ, Lee TJW, Brand A, Evans R, Vale L, Whelpton J, Rees CJ. Trial protocol for COLO-DETECT: A randomized controlled trial of lesion detection comparing colonoscopy assisted by the GI Genius™ artificial intelligence endoscopy module with standard colonoscopy. Colorectal Dis 2022; 24:1227-1237. [PMID: 35680613 PMCID: PMC9796278 DOI: 10.1111/codi.16219] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 05/24/2022] [Accepted: 05/29/2022] [Indexed: 01/01/2023]
Abstract
AIM Colorectal cancer is the second commonest cause of cancer death worldwide. Colonoscopy plays a key role in the control of colorectal cancer and, in that regard, maximizing detection (and removal) of pre-cancerous adenomas at colonoscopy is imperative. GI Genius™ (Medtronic Ltd) is a computer-aided detection system that integrates with existing endoscopy systems and improves adenoma detection during colonoscopy. COLO-DETECT aims to assess the clinical and cost effectiveness of GI Genius™ in UK routine colonoscopy practice. METHODS AND ANALYSIS Participants will be recruited from patients attending for colonoscopy at National Health Service sites in England, for clinical symptoms, surveillance or within the national Bowel Cancer Screening Programme. Randomization will involve a 1:1 allocation ratio (GI Genius™-assisted colonoscopy:standard colonoscopy) and will be stratified by age category (<60 years, 60-<74 years, ≥74 years), sex, hospital site and indication for colonoscopy. Demographic data, procedural data, histology and post-procedure patient experience and quality of life will be recorded. COLO-DETECT is designed and powered to detect clinically meaningful differences in mean adenomas per procedure and adenoma detection rate between GI Genius™-assisted colonoscopy and standard colonoscopy groups. The study will close when 1828 participants have had a complete colonoscopy. An economic evaluation will be conducted from the perspective of the National Health Service. A patient and public representative is contributing to all stages of the trial. Registered at ClinicalTrials.gov (NCT04723758) and ISRCTN (10451355). WHAT WILL THIS TRIAL ADD TO THE LITERATURE?: COLO-DETECT will be the first multi-centre randomized controlled trial evaluating GI Genius™ in real world colonoscopy practice and will, uniquely, evaluate both clinical and cost effectiveness.
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Affiliation(s)
- Alexander Seager
- South Tyneside and Sunderland NHS Foundation TrustSouth Tyneside District Hospital, South ShieldsTyne and WearUK,Newcastle University—Population Health Sciences InstituteNewcastle University Centre for CancerNewcastle Upon TyneUK
| | - Linda Sharp
- Newcastle University—Population Health Sciences InstituteNewcastle University Centre for CancerNewcastle Upon TyneUK
| | - James S. Hampton
- South Tyneside and Sunderland NHS Foundation TrustSouth Tyneside District Hospital, South ShieldsTyne and WearUK,Newcastle University—Population Health Sciences InstituteNewcastle University Centre for CancerNewcastle Upon TyneUK
| | - Laura J. Neilson
- South Tyneside and Sunderland NHS Foundation TrustSouth Tyneside District Hospital, South ShieldsTyne and WearUK
| | - Tom J. W. Lee
- Newcastle University—Population Health Sciences InstituteNewcastle University Centre for CancerNewcastle Upon TyneUK,Northumbria Healthcare NHS Foundation TrustNorth Tyneside General Hospital, North ShieldsUK
| | - Andrew Brand
- North Wales Organisation for Randomised Trials in Health (NWORTH)BangorUK
| | - Rachel Evans
- North Wales Organisation for Randomised Trials in Health (NWORTH)BangorUK
| | - Luke Vale
- Newcastle University—Health Economics Group, Population Health Sciences InstituteNewcastle University Centre for CancerNewcastle Upon TyneUK
| | - John Whelpton
- Patient and Participant Involvement RepresentativeNewcastle University‐Population Health Sciences Institute, Newcastle University Centre for CancerNewcastle Upon TyneUK
| | - Colin J. Rees
- South Tyneside and Sunderland NHS Foundation TrustSouth Tyneside District Hospital, South ShieldsTyne and WearUK,Newcastle University—Population Health Sciences InstituteNewcastle University Centre for CancerNewcastle Upon TyneUK
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18
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Gubbiotti A, Spadaccini M, Badalamenti M, Hassan C, Repici A. Key factors for improving adenoma detection rate. Expert Rev Gastroenterol Hepatol 2022; 16:819-833. [PMID: 36151898 DOI: 10.1080/17474124.2022.2128761] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Colonoscopy is a fundamental tool in colorectal cancer (CRC) prevention. Nevertheless, one-fourth of colorectal neoplasms are still missed during colonoscopy, potentially being the main reason for post-colonoscopy colorectal cancer (PCCRC). Adenoma detection rate (ADR) is currently known as the best quality indicator correlating with PCCRC incidence. AREAS COVERED We performed a literature review in order to summarize evidences investigating key factors affecting ADR: endoscopists education and training, patient management, endoscopic techniques, improved navigation (exposition defect), and enhanced lesions recognition (vision defect) were considered. EXPERT OPINION 'Traditional' factors, such as split dose bowel preparation, adequate withdrawal time, and right colon second view, held a significant impact on ADR. Several devices and technologies have been developed to promote high-quality colonoscopy, however artificial intelligence may be considered the most promising tool for ADR improvement, provided that endoscopists education and recording are guaranteed.
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Affiliation(s)
- Alessandro Gubbiotti
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy.,IRCCS Humanitas Research Hospital, Digestive Endoscopy Unit, Division of Gastroenterology, Rozzano, Italy
| | - Marco Spadaccini
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy.,IRCCS Humanitas Research Hospital, Digestive Endoscopy Unit, Division of Gastroenterology, Rozzano, Italy
| | - Matteo Badalamenti
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy.,IRCCS Humanitas Research Hospital, Digestive Endoscopy Unit, Division of Gastroenterology, Rozzano, Italy
| | - Cesare Hassan
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy.,IRCCS Humanitas Research Hospital, Digestive Endoscopy Unit, Division of Gastroenterology, Rozzano, Italy
| | - Alessandro Repici
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy.,IRCCS Humanitas Research Hospital, Digestive Endoscopy Unit, Division of Gastroenterology, Rozzano, Italy
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19
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Shaukat A, Tuskey A, Rao VL, Dominitz JA, Murad MH, Keswani RN, Bazerbachi F, Day LW. Interventions to improve adenoma detection rates for colonoscopy. Gastrointest Endosc 2022; 96:171-183. [PMID: 35680469 DOI: 10.1016/j.gie.2022.03.026] [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] [Received: 03/04/2022] [Accepted: 03/25/2022] [Indexed: 02/08/2023]
Affiliation(s)
- Aasma Shaukat
- Division of Gastroenterology, Department of Medicine, NYU Grossman School of Medicine, New York, New York, USA
| | - Anne Tuskey
- Division of Gastroenterology, Department of Medicine, University of Virginia, Arlington, Virginia, USA
| | - Vijaya L Rao
- Section of Gastroenterology, Hepatology, and Nutrition, Department of Medicine, The University of Chicago, Chicago, Illinois, USA
| | - Jason A Dominitz
- Division of Gastroenterology, Department of Medicine, Puget Sound Veterans Affairs Medical Center and University of Washington, Seattle, Washington, USA
| | - M Hassan Murad
- Division of Public Health, Infectious Diseases and Occupational Medicine, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Rajesh N Keswani
- Division of Gastroenterology, Department of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Fateh Bazerbachi
- Division of Gastroenterology, CentraCare, Interventional Endoscopy Program, St Cloud, Minnesota, USA
| | - Lukejohn W Day
- Division of Gastroenterology, Department of Medicine, Zuckerberg San Francisco General Hospital and University of San Francisco, San Francisco, California, USA
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20
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Yao L, Zhang L, Liu J, Zhou W, He C, Zhang J, Wu L, Wang H, Xu Y, Gong D, Xu M, Li X, Bai Y, Gong R, Sharma P, Yu H. Effect of an artificial intelligence-based quality improvement system on efficacy of a computer-aided detection system in colonoscopy: a four-group parallel study. Endoscopy 2022; 54:757-768. [PMID: 34823258 DOI: 10.1055/a-1706-6174] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
BACKGROUND Tandem colonoscopy studies have found that about one in five adenomas are missed at colonoscopy. It remains debatable whether the combination of a computer-aided polyp detection (CADe) system with a computer-aided quality improvement (CAQ) system for real-time monitoring of withdrawal speed results in additional benefits in adenoma detection or if the synergetic effect may be harmed due to excessive visual burden resulting from information overload. This study aimed to evaluate the interaction effect on improving the adenoma detection rate (ADR). METHODS This single-center, randomized, four-group, parallel, controlled study was performed at Renmin Hospital of Wuhan University. Between 1 July and 15 October 2020, 1076 patients were randomly allocated into four treatment groups: control 271, CADe 268, CAQ 269, and CADe plus CAQ (COMBO) 268. The primary outcome was ADR. RESULTS The ADR in the control, CADe, CAQ, and COMBO groups was 14.76 % (95 % confidence interval [CI] 10.54 to 18.98), 21.27 % (95 %CI 16.37 to 26.17), 24.54 % (95 %CI 19.39 to 29.68), and 30.60 % (95 %CI 25.08 to 36.11), respectively. The ADR was higher in the COMBO group compared with the CADe group (21.27 % vs. 30.6 %, P = 0.024, odds ratio [OR] 1.284, 95 %CI 1.033 to 1.596) but not compared with the CAQ group (24.54 % vs. 30.6 %, P = 0.213, OR 1.309, 95 %CI 0.857 to 2.000, respectively). CONCLUSIONS CAQ significantly improved the efficacy of CADe in a four-group, parallel, controlled study. No significant difference in the ADR or polyp detection rate was found between CAQ and COMBO.
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Affiliation(s)
- Liwen Yao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lihui Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Liu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Department of Nursing, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wei Zhou
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chunping He
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hongguang Wang
- Department of Gastroenterology, Jilin Renmin Hospital, Jilin, China
| | - Youming Xu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Dexin Gong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ming Xu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xun Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yutong Bai
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Rongrong Gong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Prateek Sharma
- University of Kansas School of Medicine and Veterans Affairs Medical Center, Kansas City, Missouri, United States
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.,Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
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21
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Minchenberg SB, Walradt T, Glissen Brown JR. Scoping out the future: The application of artificial intelligence to gastrointestinal endoscopy. World J Gastrointest Oncol 2022; 14:989-1001. [PMID: 35646286 PMCID: PMC9124983 DOI: 10.4251/wjgo.v14.i5.989] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 06/21/2021] [Accepted: 04/21/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a quickly expanding field in gastrointestinal endoscopy. Although there are a myriad of applications of AI ranging from identification of bleeding to predicting outcomes in patients with inflammatory bowel disease, a great deal of research has focused on the identification and classification of gastrointestinal malignancies. Several of the initial randomized, prospective trials utilizing AI in clinical medicine have centered on polyp detection during screening colonoscopy. In addition to work focused on colorectal cancer, AI systems have also been applied to gastric, esophageal, pancreatic, and liver cancers. Despite promising results in initial studies, the generalizability of most of these AI systems have not yet been evaluated. In this article we review recent developments in the field of AI applied to gastrointestinal oncology.
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Affiliation(s)
- Scott B Minchenberg
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02130, United States
| | - Trent Walradt
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02130, United States
| | - Jeremy R Glissen Brown
- Division of Gastroenterology, Beth Israel Deaconess Medical Center, Boston, MA 02130, United States
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22
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Spadaccini M, Marco AD, Franchellucci G, Sharma P, Hassan C, Repici A. Discovering the first US FDA-approved computer-aided polyp detection system. Future Oncol 2022; 18:1405-1412. [PMID: 35081745 DOI: 10.2217/fon-2021-1135] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer is the third most common cancer worldwide. Because of the slow progression of the precancerous precursors, an efficient endoscopic surveillance strategy may be expected. It seems that around one-fourth of colorectal malignancies are still missed during colonoscopy. Several endoscopic technologies have been introduced, without radical changes. Interest in the development of artificial intelligence applications in the medical field has grown in the past decade. Artificial intelligence can help to highlight a specific region of interest that needs closer examination for the identification of polyps. The aim of this review is to report the first clinical experiences with the first US FDA-approved, real-time, deep-learning, computer-aided detection system (GI Genius™, Medtronic).
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Affiliation(s)
- Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical & Research Center-IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Alessandro De Marco
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical & Research Center-IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Gianluca Franchellucci
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical & Research Center-IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Prateek Sharma
- Kansas City VA Medical Center, Gastroenterology & Hepatology, Kansas City, MO 66045, USA
| | - Cesare Hassan
- Nuovo Regina Margherita Hospital, Digestive Endoscopy Unit, Rome, Italy
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical & Research Center-IRCCS, Endoscopy Unit, Rozzano, Italy
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23
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Hann A, Meining A. Artificial Intelligence in Endoscopy. Visc Med 2022; 37:471-475. [PMID: 35083312 DOI: 10.1159/000519407] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 08/30/2021] [Indexed: 12/23/2022] Open
Abstract
Background Owing to their rapid development, artificial intelligence (AI) technologies offer a great promise for gastroenterology practice and research. At present, AI-guided image interpretation has already been used with success for endoscopic detection of early malignant lesions. Nonetheless, there are complex challenges and possible shortcomings that must be considered before full implementation can be realized. Summary In this review, the current status of AI in endoscopy is summarized. Future perspectives and open questions for further studies are stressed. Key Messages The usage of AI algorithms for polyp detection in screening colonoscopy results in a significant increase in the adenoma detection rate, mainly attributed to the identification of diminutive polyps. Computer-aided characterization of colorectal polyps accompanies the detection, but further studies are needed to evaluate the clinical benefit. In contrast to colonoscopy, usage of AI in gastroscopy is currently rather limited. Regarding other fields of endoscopic imaging, capsule endoscopy is the ideal imaging platform for AI, due to the potential of saving time in the video analysis.
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Affiliation(s)
- Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, Gastroenterology, University Hospital Würzburg, Würzburg, Germany
| | - Alexander Meining
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, Gastroenterology, University Hospital Würzburg, Würzburg, Germany
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24
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Pan H, Cai M, Liao Q, Jiang Y, Liu Y, Zhuang X, Yu Y. Artificial Intelligence-Aid Colonoscopy Vs. Conventional Colonoscopy for Polyp and Adenoma Detection: A Systematic Review of 7 Discordant Meta-Analyses. Front Med (Lausanne) 2022; 8:775604. [PMID: 35096870 PMCID: PMC8792899 DOI: 10.3389/fmed.2021.775604] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 12/20/2021] [Indexed: 12/16/2022] Open
Abstract
Objectives: Multiple meta-analyses which investigated the comparative efficacy and safety of artificial intelligence (AI)-aid colonoscopy (AIC) vs. conventional colonoscopy (CC) in the detection of polyp and adenoma have been published. However, a definitive conclusion has not yet been generated. This systematic review selected from discordant meta-analyses to draw a definitive conclusion about whether AIC is better than CC for the detection of polyp and adenoma. Methods: We comprehensively searched potentially eligible literature in PubMed, Embase, Cochrane library, and China National Knowledgement Infrastructure (CNKI) databases from their inceptions until to April 2021. Assessment of Multiple Systematic Reviews (AMSTAR) instrument was used to assess the methodological quality. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist was used to assess the reporting quality. Two investigators independently used the Jadad decision algorithm to select high-quality meta-analyses which summarized the best available evidence. Results: Seven meta-analyses met our selection criteria finally. AMSTAR score ranged from 8 to 10, and PRISMA score ranged from 23 to 26. According to the Jadad decision algorithm, two high-quality meta-analyses were selected. These two meta-analyses suggested that AIC was superior to CC for colonoscopy outcomes, especially for polyp detection rate (PDR) and adenoma detection rate (ADR). Conclusion: Based on the best available evidence, we conclude that AIC should be preferentially selected for the route screening of colorectal lesions because it has potential value of increasing the polyp and adenoma detection. However, the continued improvement of AIC in differentiating the shape and pathology of colorectal lesions is needed.
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Affiliation(s)
- Hui Pan
- Department of Endoscopy, Shanghai Jiangong Hospital, Shanghai, China
| | - Mingyan Cai
- Endoscopy Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Qi Liao
- Department of Gastroenterology, Shanghai Jiangong Hospital, Shanghai, China
| | - Yong Jiang
- Department of Surgery, Shanghai Jiangong Hospital, Shanghai, China
| | - Yige Liu
- Department of Endoscopy, Shanghai Jiangong Hospital, Shanghai, China
| | - Xiaolong Zhuang
- Department of Endoscopy, Shanghai Jiangong Hospital, Shanghai, China
| | - Ying Yu
- Department of Endoscopy, Shanghai Jiangong Hospital, Shanghai, China
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25
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Soons E, Rath T, Hazewinkel Y, van Dop WA, Esposito D, Testoni PA, Siersema PD. Real-time colorectal polyp detection using a novel computer-aided detection system (CADe): a feasibility study. Int J Colorectal Dis 2022; 37:2219-2228. [PMID: 36163514 PMCID: PMC9560918 DOI: 10.1007/s00384-022-04258-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/18/2022] [Indexed: 02/04/2023]
Abstract
BACKGROUND AND AIMS Colonoscopy aims to early detect and remove precancerous colorectal polyps, thereby preventing development of colorectal cancer (CRC). Recently, computer-aided detection (CADe) systems have been developed to assist endoscopists in polyp detection during colonoscopy. The aim of this study was to investigate feasibility and safety of a novel CADe system during real-time colonoscopy in three European tertiary referral centers. METHODS Ninety patients undergoing colonoscopy assisted by a real-time CADe system (DISCOVERY; Pentax Medical, Tokyo, Japan) were prospectively included. The CADe system was turned on only at withdrawal, and its output was displayed on secondary monitor. To study feasibility, inspection time, polyp detection rate (PDR), adenoma detection rate (ADR), sessile serrated lesion (SSL) detection rate (SDR), and the number of false positives were recorded. To study safety, (severe) adverse events ((S)AEs) were collected. Additionally, user friendliness was rated from 1 (worst) to 10 (best) by endoscopists. RESULTS Mean inspection time was 10.8 ± 4.3 min, while PDR was 55.6%, ADR 28.9%, and SDR 11.1%. The CADe system users estimated that < 20 false positives occurred in 81 colonoscopy procedures (90%). No (S)AEs related to the CADe system were observed during the 30-day follow-up period. User friendliness was rated as good, with a median score of 8/10. CONCLUSION Colonoscopy with this novel CADe system in a real-time setting was feasible and safe. Although PDR and SDR were high compared to previous studies with other CADe systems, future randomized controlled trials are needed to confirm these detection rates. The high SDR is of particular interest since interval CRC has been suggested to develop frequently through the serrated neoplasia pathway. CLINICAL TRIAL REGISTRATION The study was registered in the Dutch Trial Register (reference number: NL8788).
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Affiliation(s)
- E. Soons
- Department of Gastroenterology and Hepatology, Radboud Institute for Health Sciences, Radboud University Medical Center, 9101, 6500 HB Nijmegen, the Netherlands
| | - T. Rath
- Department of Internal Medicine 1, Division of Gastroenterology, Friedrich-Alexander-University, Ludwig Demling Endoscopy Center of Excellence, Erlangen Nuernberg, Germany
| | - Y. Hazewinkel
- Department of Gastroenterology and Hepatology, Radboud Institute for Health Sciences, Radboud University Medical Center, 9101, 6500 HB Nijmegen, the Netherlands
| | - W. A. van Dop
- Department of Gastroenterology and Hepatology, Radboud Institute for Health Sciences, Radboud University Medical Center, 9101, 6500 HB Nijmegen, the Netherlands
| | - D. Esposito
- Gastroenterology and Gastrointestinal Endoscopy Unit, Vita-Salute San Raffaele University, Scientific Institute San Raffaele, Milan, Italy
| | - P. A. Testoni
- Gastroenterology and Gastrointestinal Endoscopy Unit, Vita-Salute San Raffaele University, Scientific Institute San Raffaele, Milan, Italy
| | - P. D. Siersema
- Department of Gastroenterology and Hepatology, Radboud Institute for Health Sciences, Radboud University Medical Center, 9101, 6500 HB Nijmegen, the Netherlands
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26
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Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience. Eur J Gastroenterol Hepatol 2021; 33:e662-e669. [PMID: 34034272 PMCID: PMC8734627 DOI: 10.1097/meg.0000000000002209] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
AIM The use of artificial intelligence represents an objective approach to increase endoscopist's adenoma detection rate (ADR) and limit interoperator variability. In this study, we evaluated a newly developed deep convolutional neural network (DCNN) for automated detection of colorectal polyps ex vivo as well as in a first in-human trial. METHODS For training of the DCNN, 116 529 colonoscopy images from 278 patients with 788 different polyps were collected. A subset of 10 467 images containing 504 different polyps were manually annotated and treated as the gold standard. An independent set of 45 videos consisting of 15 534 single frames was used for ex vivo performance testing. In vivo real-time detection of colorectal polyps during routine colonoscopy by the DCNN was tested in 42 patients in a back-to-back approach. RESULTS When analyzing the test set of 15 534 single frames, the DCNN's sensitivity and specificity for polyp detection and localization within the frame was 90% and 80%, respectively, with an area under the curve of 0.92. In vivo, baseline polyp detection rate and ADR were 38% and 26% and significantly increased to 50% (P = 0.023) and 36% (P = 0.044), respectively, with the use of the DCNN. Of the 13 additionally with the DCNN detected lesions, the majority were diminutive and flat, among them three sessile serrated adenomas. CONCLUSION This newly developed DCNN enables highly sensitive automated detection of colorectal polyps both ex vivo and during first in-human clinical testing and could potentially increase the detection of colorectal polyps during colonoscopy.
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27
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Deliwala SS, Hamid K, Barbarawi M, Lakshman H, Zayed Y, Kandel P, Malladi S, Singh A, Bachuwa G, Gurvits GE, Chawla S. Artificial intelligence (AI) real-time detection vs. routine colonoscopy for colorectal neoplasia: a meta-analysis and trial sequential analysis. Int J Colorectal Dis 2021; 36:2291-2303. [PMID: 33934173 DOI: 10.1007/s00384-021-03929-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/07/2021] [Indexed: 02/04/2023]
Abstract
GOALS AND BACKGROUND Studies analyzing artificial intelligence (AI) in colonoscopies have reported improvements in detecting colorectal cancer (CRC) lesions, however its utility in the realworld remains limited. In this systematic review and meta-analysis, we evaluate the efficacy of AI-assisted colonoscopies against routine colonoscopy (RC). STUDY We performed an extensive search of major databases (through January 2021) for randomized controlled trials (RCTs) reporting adenoma and polyp detection rates. Odds ratio (OR) and standardized mean differences (SMD) with 95% confidence intervals (CIs) were reported. Additionally, trial sequential analysis (TSA) was performed to guard against errors. RESULTS Six RCTs were included (4996 participants). The mean age (SD) was 51.99 (4.43) years, and 49% were females. Detection rates favored AI over RC for adenomas (OR 1.77; 95% CI: 1.570-2.08) and polyps (OR 1.91; 95% CI: 1.68-2.16). Secondary outcomes including mean number of adenomas (SMD 0.23; 95% CI: 0.18-0.29) and polyps (SMD 0.23; 95% CI: 0.17-0.29) detected per procedure favored AI. However, RC outperformed AI in detecting pedunculated polyps. Withdrawal times (WTs) favored AI when biopsies were included, while WTs without biopsies, cecal intubation times, and bowel preparation adequacy were similar. CONCLUSIONS Colonoscopies equipped with AI detection algorithms could significantly detect previously missed adenomas and polyps while retaining the ability to self-assess and improve periodically. More effective clearance of diminutive adenomas may allow lengthening in surveillance intervals, reducing the burden of surveillance colonoscopies, and increasing its accessibility to those at higher risk. TSA ruled out the risk for false-positive results and confirmed a sufficient sample size to detect the observed effect. Currently, these findings suggest that AI-assisted colonoscopy can serve as a useful proxy to address critical gaps in CRC identification.
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Affiliation(s)
- Smit S Deliwala
- Department of Internal Medicine, Michigan State University at Hurley Medical Center, Two Hurley Plaza, Ste 212, Flint, MI, 48503, USA.
| | - Kewan Hamid
- Department of Internal Medicine/Pediatrics, Michigan State University at Hurley Medical Center, Flint, MI, USA
| | - Mahmoud Barbarawi
- Department of Internal Medicine, Michigan State University at Hurley Medical Center, Two Hurley Plaza, Ste 212, Flint, MI, 48503, USA
| | - Harini Lakshman
- Department of Internal Medicine, Michigan State University at Hurley Medical Center, Two Hurley Plaza, Ste 212, Flint, MI, 48503, USA
| | - Yazan Zayed
- Department of Internal Medicine, Michigan State University at Hurley Medical Center, Two Hurley Plaza, Ste 212, Flint, MI, 48503, USA
| | - Pujan Kandel
- Department of Internal Medicine, Michigan State University at Hurley Medical Center, Two Hurley Plaza, Ste 212, Flint, MI, 48503, USA
| | - Srikanth Malladi
- Department of Internal Medicine/Pediatrics, Michigan State University at Hurley Medical Center, Flint, MI, USA
| | - Adiraj Singh
- Department of Internal Medicine/Pediatrics, Michigan State University at Hurley Medical Center, Flint, MI, USA
| | - Ghassan Bachuwa
- Department of Internal Medicine, Michigan State University at Hurley Medical Center, Two Hurley Plaza, Ste 212, Flint, MI, 48503, USA
| | - Grigoriy E Gurvits
- Department of Internal Medicine - Division of Gastroenterology, New York University/Langone Medical Center, New York, NY, USA
| | - Saurabh Chawla
- Department of Internal Medicine - Division of Gastroenterology, Emory University, Atlanta, GA, USA
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28
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Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
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Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
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Nogueira-Rodríguez A, Domínguez-Carbajales R, Campos-Tato F, Herrero J, Puga M, Remedios D, Rivas L, Sánchez E, Iglesias Á, Cubiella J, Fdez-Riverola F, López-Fernández H, Reboiro-Jato M, Glez-Peña D. Real-time polyp detection model using convolutional neural networks. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06496-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
AbstractColorectal cancer is a major health problem, where advances towards computer-aided diagnosis (CAD) systems to assist the endoscopist can be a promising path to improvement. Here, a deep learning model for real-time polyp detection based on a pre-trained YOLOv3 (You Only Look Once) architecture and complemented with a post-processing step based on an object-tracking algorithm to reduce false positives is reported. The base YOLOv3 network was fine-tuned using a dataset composed of 28,576 images labelled with locations of 941 polyps that will be made public soon. In a frame-based evaluation using isolated images containing polyps, a general F1 score of 0.88 was achieved (recall = 0.87, precision = 0.89), with lower predictive performance in flat polyps, but higher for sessile, and pedunculated morphologies, as well as with the usage of narrow band imaging, whereas polyp size < 5 mm does not seem to have significant impact. In a polyp-based evaluation using polyp and normal mucosa videos, with a positive criterion defined as the presence of at least one 50-frames-length (window size) segment with a ratio of 75% of frames with predicted bounding boxes (frames positivity), 72.61% of sensitivity (95% CI 68.99–75.95) and 83.04% of specificity (95% CI 76.70–87.92) were achieved (Youden = 0.55, diagnostic odds ratio (DOR) = 12.98). When the positive criterion is less stringent (window size = 25, frames positivity = 50%), sensitivity reaches around 90% (sensitivity = 89.91%, 95% CI 87.20–91.94; specificity = 54.97%, 95% CI 47.49–62.24; Youden = 0.45; DOR = 10.76). The object-tracking algorithm has demonstrated a significant improvement in specificity whereas maintaining sensitivity, as well as a marginal impact on computational performance. These results suggest that the model could be effectively integrated into a CAD system.
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Yoo BS, D'Souza SM, Houston K, Patel A, Lau J, Elmahdi A, Parekh PJ, Johnson D. Artificial intelligence and colonoscopy − enhancements and improvements. Artif Intell Gastrointest Endosc 2021; 2:157-167. [DOI: 10.37126/aige.v2.i4.157] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Revised: 06/21/2021] [Accepted: 07/23/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence is a technology that processes and analyzes information with reproducibility and accuracy. Its application in medicine, especially in the field of gastroenterology, has great potential to facilitate in diagnosis of various disease states. Currently, the role of artificial intelligence as it pertains to colonoscopy revolves around enhanced polyp detection and characterization. The aim of this article is to review the current and potential future applications of artificial intelligence for enhanced quality of detection for colorectal neoplasia.
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Affiliation(s)
- Byung Soo Yoo
- Department of Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Steve M D'Souza
- Department of Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Kevin Houston
- Department of Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Ankit Patel
- Department of Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - James Lau
- Department of Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Alsiddig Elmahdi
- Department of Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Parth J Parekh
- Division of Gastroenterology, Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23505, United States
| | - David Johnson
- Division of Gastroenterology, Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23505, United States
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31
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Zhao SB, Yang W, Wang SL, Pan P, Wang RD, Chang X, Sun ZQ, Fu XH, Shang H, Wu JR, Chen LZ, Chang J, Song P, Miao YL, He SX, Miao L, Jiang HQ, Wang W, Yang X, Dong YH, Lin H, Chen Y, Gao J, Meng QQ, Jin ZD, Li ZS, Bai Y. Establishment and validation of a computer-assisted colonic polyp localization system based on deep learning. World J Gastroenterol 2021; 27:5232-5246. [PMID: 34497447 PMCID: PMC8384745 DOI: 10.3748/wjg.v27.i31.5232] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/10/2021] [Accepted: 07/20/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Artificial intelligence in colonoscopy is an emerging field, and its application may help colonoscopists improve inspection quality and reduce the rate of missed polyps and adenomas. Several deep learning-based computer-assisted detection (CADe) techniques were established from small single-center datasets, and unrepresentative learning materials might confine their application and generalization in wide practice. Although CADes have been reported to identify polyps in colonoscopic images and videos in real time, their diagnostic performance deserves to be further validated in clinical practice.
AIM To train and test a CADe based on multicenter high-quality images of polyps and preliminarily validate it in clinical colonoscopies.
METHODS With high-quality screening and labeling from 55 qualified colonoscopists, a dataset consisting of over 71000 images from 20 centers was used to train and test a deep learning-based CADe. In addition, the real-time diagnostic performance of CADe was tested frame by frame in 47 unaltered full-ranged videos that contained 86 histologically confirmed polyps. Finally, we conducted a self-controlled observational study to validate the diagnostic performance of CADe in real-world colonoscopy with the main outcome measure of polyps per colonoscopy in Changhai Hospital.
RESULTS The CADe was able to identify polyps in the test dataset with 95.0% sensitivity and 99.1% specificity. For colonoscopy videos, all 86 polyps were detected with 92.2% sensitivity and 93.6% specificity in frame-by-frame analysis. In the prospective validation, the sensitivity of CAD in identifying polyps was 98.4% (185/188). Folds, reflections of light and fecal fluid were the main causes of false positives in both the test dataset and clinical colonoscopies. Colonoscopists can detect more polyps (0.90 vs 0.82, P < 0.001) and adenomas (0.32 vs 0.30, P = 0.045) with the aid of CADe, particularly polyps < 5 mm and flat polyps (0.65 vs 0.57, P < 0.001; 0.74 vs 0.67, P = 0.001, respectively). However, high efficacy is not realized in colonoscopies with inadequate bowel preparation and withdrawal time (P = 0.32; P = 0.16, respectively).
CONCLUSION CADe is feasible in the clinical setting and might help endoscopists detect more polyps and adenomas, and further confirmation is warranted.
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Affiliation(s)
- Sheng-Bing Zhao
- Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Wei Yang
- Tencent AI Lab, National Open Innovation Platform for Next Generation Artificial Intelligence on Medical Imaging, Shenzhen 518063, Guangdong Province, China
| | - Shu-Ling Wang
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Peng Pan
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Run-Dong Wang
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Xin Chang
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Zhong-Qian Sun
- Tencent AI Lab, National Open Innovation Platform for Next Generation Artificial Intelligence on Medical Imaging, Shenzhen 518063, Guangdong Province, China
| | - Xing-Hui Fu
- Tencent AI Lab, National Open Innovation Platform for Next Generation Artificial Intelligence on Medical Imaging, Shenzhen 518063, Guangdong Province, China
| | - Hong Shang
- Tencent AI Lab, National Open Innovation Platform for Next Generation Artificial Intelligence on Medical Imaging, Shenzhen 518063, Guangdong Province, China
| | - Jian-Rong Wu
- Tencent Healthcare (Shenzhen) Co. LTD., Shenzhen 518063, Guangdong Province, China
| | - Li-Zhu Chen
- Tencent Healthcare (Shenzhen) Co. LTD., Shenzhen 518063, Guangdong Province, China
| | - Jia Chang
- Tencent Healthcare (Shenzhen) Co. LTD., Shenzhen 518063, Guangdong Province, China
| | - Pu Song
- Tencent Healthcare (Shenzhen) Co. LTD., Shenzhen 518063, Guangdong Province, China
| | - Ying-Lei Miao
- Department of Gastroenterology, The First Affiliated Hospital of Kunming Medical University, Kunming 650000, Yunnan Province, China
| | - Shui-Xiang He
- Department of Gastroenterology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi Province, China
| | - Lin Miao
- Institute of Digestive Endoscopy and Medical Center for Digestive Disease, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210011, Jiangsu Province, China
| | - Hui-Qing Jiang
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Hebei Key Laboratory of Gastroenterology, Hebei Institute of Gastroenterology, Shijiazhuang 050000, Hebei Province, China
| | - Wen Wang
- Department of Gastroenterology, 900th Hospital of Joint Logistics Support Force, Fuzhou 350025, Fujian Province, China
| | - Xia Yang
- Department of Gastroenterology, No. 905 Hospital of The Chinese People's Liberation Army, Shanghai 200050, China
| | - Yuan-Hang Dong
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Han Lin
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Yan Chen
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Jie Gao
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Qian-Qian Meng
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Zhen-Dong Jin
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Zhao-Shen Li
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Yu Bai
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
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Computer-Aided Colon Polyp Detection on High Resolution Colonoscopy Using Transfer Learning Techniques. SENSORS 2021; 21:s21165315. [PMID: 34450756 PMCID: PMC8402119 DOI: 10.3390/s21165315] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/01/2021] [Accepted: 08/04/2021] [Indexed: 01/10/2023]
Abstract
Colonoscopies reduce the incidence of colorectal cancer through early recognition and resecting of the colon polyps. However, the colon polyp miss detection rate is as high as 26% in conventional colonoscopy. The search for methods to decrease the polyp miss rate is nowadays a paramount task. A number of algorithms or systems have been developed to enhance polyp detection, but few are suitable for real-time detection or classification due to their limited computational ability. Recent studies indicate that the automated colon polyp detection system is developing at an astonishing speed. Real-time detection with classification is still a yet to be explored field. Newer image pattern recognition algorithms with convolutional neuro-network (CNN) transfer learning has shed light on this topic. We proposed a study using real-time colonoscopies with the CNN transfer learning approach. Several multi-class classifiers were trained and mAP ranged from 38% to 49%. Based on an Inception v2 model, a detector adopting a Faster R-CNN was trained. The mAP of the detector was 77%, which was an improvement of 35% compared to the same type of multi-class classifier. Therefore, our results indicated that the polyp detection model could attain a high accuracy, but the polyp type classification still leaves room for improvement.
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Shah N, Jyala A, Patel H, Makker J. Utility of artificial intelligence in colonoscopy. Artif Intell Gastrointest Endosc 2021; 2:79-88. [DOI: 10.37126/aige.v2.i3.79] [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: 06/02/2021] [Revised: 06/20/2021] [Accepted: 06/28/2021] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer is one of the major causes of death worldwide. Colonoscopy is the most important tool that can identify neoplastic lesion in early stages and resect it in a timely manner which helps in reducing mortality related to colorectal cancer. However, the quality of colonoscopy findings depends on the expertise of the endoscopist and thus the rate of missed adenoma or polyp cannot be controlled. It is desirable to standardize the quality of colonoscopy by reducing the number of missed adenoma/polyps. Introduction of artificial intelligence (AI) in the field of medicine has become popular among physicians nowadays. The application of AI in colonoscopy can help in reducing miss rate and increasing colorectal cancer detection rate as per recent studies. Moreover, AI assistance during colonoscopy has also been utilized in patients with inflammatory bowel disease to improve diagnostic accuracy, assessing disease severity and predicting clinical outcomes. We conducted a literature review on the available evidence on use of AI in colonoscopy. In this review article, we discuss about the principles, application, limitations, and future aspects of AI in colonoscopy.
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Affiliation(s)
- Niel Shah
- Department of Internal Medicine, BronxCare Hospital Center, Bronx, NY 10457, United States
| | - Abhilasha Jyala
- Department of Internal Medicine, BronxCare Hospital Center, Bronx, NY 10457, United States
| | - Harish Patel
- Department of Internal Medicine, Gastroenterology, BronxCare Hospital Center, Bronx, NY 10457, United States
| | - Jasbir Makker
- Department of Internal Medicine, Gastroenterology, BronxCare Hospital Center, Bronx, NY 10457, United States
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34
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Shah N, Jyala A, Patel H, Makker J. Utility of artificial intelligence in colonoscopy. Artif Intell Gastrointest Endosc 2021. [DOI: 10.37126/aige.v2.i3.78] [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: 12/19/2022] Open
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Mitsala A, Tsalikidis C, Pitiakoudis M, Simopoulos C, Tsaroucha AK. Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era. ACTA ACUST UNITED AC 2021; 28:1581-1607. [PMID: 33922402 PMCID: PMC8161764 DOI: 10.3390/curroncol28030149] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 04/09/2021] [Accepted: 04/20/2021] [Indexed: 12/24/2022]
Abstract
The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.
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Affiliation(s)
- Athanasia Mitsala
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
- Correspondence: ; Tel.: +30-6986423707
| | - Christos Tsalikidis
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Michail Pitiakoudis
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Constantinos Simopoulos
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Alexandra K. Tsaroucha
- Laboratory of Experimental Surgery & Surgical Research, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece;
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Ashat M, Klair JS, Singh D, Murali AR, Krishnamoorthi R. Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis. Endosc Int Open 2021; 9:E513-E521. [PMID: 33816771 PMCID: PMC7969136 DOI: 10.1055/a-1341-0457] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 11/23/2020] [Indexed: 02/06/2023] Open
Abstract
Background and study aims With the advent of deep neural networks (DNN) learning, the field of artificial intelligence (AI) is rapidly evolving. Recent randomized controlled trials (RCT) have investigated the influence of integrating AI in colonoscopy and its impact on adenoma detection rates (ADRs) and polyp detection rates (PDRs). We performed a systematic review and meta-analysis to reliably assess if the impact is statistically significant enough to warrant the adoption of AI -assisted colonoscopy (AIAC) in clinical practice. Methods We conducted a comprehensive search of multiple electronic databases and conference proceedings to identify RCTs that compared outcomes between AIAC and conventional colonoscopy (CC). The primary outcome was ADR. The secondary outcomes were PDR and total withdrawal time (WT). Results Six RCTs (comparing AIAC vs CC) with 5058 individuals undergoing average-risk screening colonoscopy were included in the meta-analysis. ADR was significantly higher with AIAC compared to CC (33.7 % versus 22.9 %; odds ratio (OR) 1.76, 95 % confidence interval (CI) 1.55-2.00; I 2 = 28 %). Similarly, PDR was significantly higher with AIAC (45.6 % versus 30.6 %; OR 1.90, 95 %CI, 1.68-2.15, I 2 = 0 %). The overall WT was higher for AIAC compared to CC (mean difference [MD] 0.46 (0.00-0.92) minutes, I 2 = 94 %). Conclusions There is an increase in adenoma and polyp detection with the utilization of AIAC.
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Affiliation(s)
- Munish Ashat
- Department of Gastroenterology and Hepatology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States
| | - Jagpal Singh Klair
- Digestive Disease Institute, Virginia Mason Medical Center, Seattle, Washington, United States
| | - Dhruv Singh
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, New York, United States
| | - Arvind Rangarajan Murali
- Department of Gastroenterology and Hepatology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States
| | - Rajesh Krishnamoorthi
- Digestive Disease Institute, Virginia Mason Medical Center, Seattle, Washington, United States
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Wang X, Huang J, Ji X, Zhu Z. [Application of artificial intelligence for detection and classification of colon polyps]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2021; 41:310-313. [PMID: 33624608 DOI: 10.12122/j.issn.1673-4254.2021.02.22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
Colorectal cancer is one of the most common cancers worldwide, and colonoscopy has proven to be a preferable modality for screening and surveillance of colorectal cancer. This review discusses the clinical application of artificial intelligence (AI) and computer-aided diagnosis for automated colonoscopic detection and diagnosis of colorectal polyps for better understanding of the application of AI-based computer-aided diagnosis systems especially in terms of machine learning, deep learning and convolutional neural network for screening and surveillance of colorectal cancer.
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Affiliation(s)
- X Wang
- Information Center, First Affiliated Hospital of Kunming Medical University, Kunming 65003, China
| | - J Huang
- Department of Oncology, First Affiliated Hospital of Kunming Medical University, Kunming 65003, China
| | - X Ji
- Day Surgery Center, First Affiliated Hospital of Kunming Medical University, Kunming 65003, China
| | - Z Zhu
- Day Surgery Center, First Affiliated Hospital of Kunming Medical University, Kunming 65003, China
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38
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Radaelli F, Paggi S. Artificial intelligence and the endoscopist's skill and proficiency for polyp detection: no winner one without the other! Transl Gastroenterol Hepatol 2021; 6:7. [PMID: 33409401 DOI: 10.21037/tgh.2019.01.08] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 01/25/2019] [Indexed: 01/16/2023] Open
Affiliation(s)
- Franco Radaelli
- Division of Digestive Endoscopy and Gastroenterology, Valduce Hospital, Como, Italy
| | - Silvia Paggi
- Division of Digestive Endoscopy and Gastroenterology, Valduce Hospital, Como, Italy
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Guo Z, Nemoto D, Zhu X, Li Q, Aizawa M, Utano K, Isohata N, Endo S, Kawarai Lefor A, Togashi K. Polyp detection algorithm can detect small polyps: Ex vivo reading test compared with endoscopists. Dig Endosc 2021; 33:162-169. [PMID: 32173917 DOI: 10.1111/den.13670] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 03/09/2020] [Accepted: 03/12/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND AND STUDY AIMS Small polyps are occasionally missed during colonoscopy. This study was conducted to validate the diagnostic performance of a polyp-detection algorithm to alert endoscopists to unrecognized lesions. METHODS A computer-aided detection (CADe) algorithm was developed based on convolutional neural networks using training data from 1991 still colonoscopy images from 283 subjects with adenomatous polyps. The CADe algorithm was evaluated on a validation dataset including 50 short videos with 1-2 polyps (3.5 ± 1.5 mm, range 2-8 mm) and 50 videos without polyps. Two expert colonoscopists and two physicians in training separately read the same videos, blinded to the presence of polyps. The CADe algorithm was also evaluated using eight full videos with polyps and seven full videos without a polyp. RESULTS The per-video sensitivity of CADe for polyp detection was 88% and the per-frame false-positive rate was 2.8%, with a confidence level of ≥30%. The per-video sensitivity of both experts was 88%, and the sensitivities of the two physicians in training were 84% and 76%. For each reader, the frames with missed polyps appearing on short videos were significantly less than the frames with detected polyps, but no trends were observed regarding polyp size, morphology or color. For full video readings, per-polyp sensitivity was 100% with a per-frame false-positive rate of 1.7%, and per-frame specificity of 98.3%. CONCLUSIONS The sensitivity of CADe to detect small polyps was almost equivalent to experts and superior to physicians in training. A clinical trial using CADe is warranted.
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Affiliation(s)
- Zhe Guo
- Biomedical Information Engineering Lab, The University of Aizu, Fukushima, Japan
| | - Daiki Nemoto
- Department of Coloproctology, Aizu Medical Center, Fukushima Medical University, Fukushima, Japan
| | - Xin Zhu
- Biomedical Information Engineering Lab, The University of Aizu, Fukushima, Japan
| | - Qin Li
- Biomedical Information Engineering Lab, The University of Aizu, Fukushima, Japan
| | - Masato Aizawa
- Department of Coloproctology, Aizu Medical Center, Fukushima Medical University, Fukushima, Japan
| | - Kenichi Utano
- Department of Coloproctology, Aizu Medical Center, Fukushima Medical University, Fukushima, Japan
| | - Noriyuki Isohata
- Department of Coloproctology, Aizu Medical Center, Fukushima Medical University, Fukushima, Japan
| | - Shungo Endo
- Department of Coloproctology, Aizu Medical Center, Fukushima Medical University, Fukushima, Japan
| | | | - Kazutomo Togashi
- Department of Coloproctology, Aizu Medical Center, Fukushima Medical University, Fukushima, Japan
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40
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Luo Y, Zhang Y, Liu M, Lai Y, Liu P, Wang Z, Xing T, Huang Y, Li Y, Li A, Wang Y, Luo X, Liu S, Han Z. Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study. J Gastrointest Surg 2021; 25:2011-2018. [PMID: 32968933 PMCID: PMC8321985 DOI: 10.1007/s11605-020-04802-4] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 09/09/2020] [Indexed: 01/31/2023]
Abstract
BACKGROUND AND AIMS Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment. METHODS The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov . (NCT047126265). RESULTS In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions. CONCLUSIONS A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT047126265.
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Affiliation(s)
- Yuchen Luo
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China
| | - Yi Zhang
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China
| | - Ming Liu
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China
| | - Yihong Lai
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China
| | - Panpan Liu
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China
| | - Zhen Wang
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China
| | - Tongyin Xing
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China
| | - Ying Huang
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China
| | - Yue Li
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China
| | - Aiming Li
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China
| | - Yadong Wang
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China
| | - Xiaobei Luo
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China
| | - Side Liu
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China
| | - Zelong Han
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China
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Marella HK, Saleem N, Tombazzi C. Impact of Moderate versus Deep Sedation and Trainee Participation on Adenoma Detection Rate-Analysis of a Veteran Population. Clin Endosc 2020; 54:250-255. [PMID: 33317225 PMCID: PMC8039744 DOI: 10.5946/ce.2020.091] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 08/26/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND/AIMS The adenoma detection rate (ADR) is used as a quality indicator for screening and surveillance colonoscopy. The study aimed to determine if moderate versus deep sedation affects the outcomes of the ADR and other quality metrics in the veteran population. METHODS A retrospective review of colonoscopies performed at Memphis Veterans Affairs Medical Center over a one-year period was conducted. A total of 900 colonoscopy reports were reviewed. After exclusion criteria, a total of 229 index, average-risk screening colonoscopies were identified. Data were collected to determine the impact of moderate (benzodiazepine plus opioids) versus deep (propofol) sedation on the ADR, polyp detection rate (PDR), and withdrawal time. RESULTS Among 229 screening colonoscopies, 103 (44.9%) used moderate sedation while 126 (55%) were done under deep sedation. The ADR and PDR were not significantly different between moderate versus deep sedation at 35.9% vs. 37.3% (p=0.82) and 58.2% vs. 48.4% (p=0.13), respectively. Similarly, there was no significant difference in withdrawal time between moderate and deep sedation (13.4 min vs. 14 min, p=0.56) during screening colonoscopies. CONCLUSION In veterans undergoing index, average-risk screening colonoscopies, the quality metrics of the ADR, PDR, and withdrawal time are not influenced by deep sedation compared with moderate sedation.
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Affiliation(s)
- Hemnishil K Marella
- Department of Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Nasir Saleem
- Division of Gastroenterology and Hepatology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Claudio Tombazzi
- Division of Gastroenterology and Hepatology, University of Tennessee Health Science Center, Memphis, TN, USA
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Liu P, Wang P, Glissen Brown JR, Berzin TM, Zhou G, Liu W, Xiao X, Chen Z, Zhang Z, Zhou C, Lei L, Xiong F, Li L, Liu X. The single-monitor trial: an embedded CADe system increased adenoma detection during colonoscopy: a prospective randomized study. Therap Adv Gastroenterol 2020; 13:1756284820979165. [PMID: 33403003 PMCID: PMC7745558 DOI: 10.1177/1756284820979165] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 11/16/2020] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Computer-aided detection (CADe) of colon polyps has been demonstrated to improve colon polyp and adenoma detection during colonoscopy by indicating the location of a given polyp on a parallel monitor. The aim of this study was to investigate whether embedding the CADe system into the primary colonoscopy monitor may serve to increase polyp and adenoma detection, without increasing physician fatigue level. METHODS Consecutive patients presenting for colonoscopies were prospectively randomized to undergo routine colonoscopy with or without the assistance of a real-time polyp detection CADe system. Fatigue level was evaluated from score 0 to 10 by the performing endoscopists after each colonoscopy procedure. The main outcome was adenoma detection rate (ADR). RESULTS Out of 790 patients analyzed, 397 were randomized to routine colonoscopy (control group), and 393 to a colonoscopy with computer-aided diagnosis (CADe group). The ADRs were 20.91% and 29.01%, respectively (OR = 1.546, 95% CI 1.116-2.141, p = 0.009). The average number of adenomas per colonoscopy (APC) was 0.29 and 0.48, respectively (Change Folds = 1.64, 95% CI 1.299-2.063, p < 0.001). The improvement in polyp detection was mainly due to increased detection of non-advanced diminutive adenomas, serrated adenoma and hyperplastic polyps. The fatigue score for each procedure was 3.28 versus 3.40 for routine and CADe group, p = 0.357. CONCLUSIONS A real-time CADe system employed on the primary endoscopy monitor may lead to improvements in ADR and polyp detection rate without increasing fatigue level during colonoscopy. The integration of a low-latency and high-performance CADe systems may serve as an effective quality assurance tool during colonoscopy. www.chictr.org.cn number, ChiCTR1800018058.
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Affiliation(s)
- Peixi Liu
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | | | - Jeremy R. Glissen Brown
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Tyler M. Berzin
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Guanyu Zhou
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Weihui Liu
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Xun Xiao
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Ziyang Chen
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Zhihong Zhang
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Chao Zhou
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Lei Lei
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Fei Xiong
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Liangping Li
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Xiaogang Liu
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
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Facciorusso A, Buccino VR, Tonti P, Licinio R, Del Prete V, Neve V, Di Maso M, Muscatiello N. Impact of fellow participation on colon adenoma detection rates: a multicenter randomized trial. Gastrointest Endosc 2020; 92:1228-1235. [PMID: 32433915 DOI: 10.1016/j.gie.2020.05.015] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 05/03/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS There are limited and conflicting data on the impact of fellow participation in improving the colon adenoma detection rate. We performed a multicenter randomized controlled trial to evaluate whether fellow involvement might have a beneficial effect on adenoma detection rate. METHODS The trial was conducted at 4 tertiary hospitals between April and December 2019. Eight hundred twelve patients were randomized to undergo colonoscopy performed by a fellow under the supervision of a staff endoscopist or by an attending physician alone. RESULTS No significant differences in demographic or adenoma risk factors were detected between the 2 groups. The adenoma detection rate in the intervention group was 44.8% versus 37.1% in the control arm (P = .02). The mean number of adenomas per colonoscopy was significantly higher in the intervention group (0.65 ± 0.3 vs 0.53 ± 0.2 in the control arm, P < .001). The polyp detection rate was 69.7% in the intervention group and 62.5% in the control arm (P = .03), whereas rates of advanced and sessile/serrated adenoma detection were not different between the trial arms (P = .50 and .42, respectively). In the subgroup of more experienced fellows, the adenoma detection rate and polyp detection rate were 49.5% and 75.7%, respectively. No difference was observed between less-experienced fellows and attending physicians alone (P = .53 and 0.86, respectively). The level of bowel preparation and fellow involvement were significant predictors of increased adenoma detection rate in a multivariate analysis. CONCLUSIONS Our multicenter trial represents the first prospective validation of the beneficial role of fellow involvement in colonoscopy procedures. (Clinical trial registration number: NCT03908229.).
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Affiliation(s)
- Antonio Facciorusso
- Gastroenterology Unit, Department of Medical Sciences, University of Foggia, Foggia, Italy
| | | | - Paolo Tonti
- Gastroenterology Unit, Department of Medical Sciences, University of Foggia, Foggia, Italy
| | | | | | - Viviana Neve
- Endoscopy Unit, Ospedale A. Perrino, Brindisi, Italy
| | - Marianna Di Maso
- Endoscopy Unit, Ospedale Teresa Masselli Mascia, San Severo, Italy
| | - Nicola Muscatiello
- Gastroenterology Unit, Department of Medical Sciences, University of Foggia, Foggia, Italy
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44
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Tang CP, Shao PP, Hsieh YH, Leung FW. A review of water exchange and artificial intelligence in improving adenoma detection. Tzu Chi Med J 2020; 33:108-114. [PMID: 33912406 PMCID: PMC8059458 DOI: 10.4103/tcmj.tcmj_88_20] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 05/22/2020] [Accepted: 06/06/2020] [Indexed: 12/21/2022] Open
Abstract
Water exchange (WE) and artificial intelligence (AI) have made critical advances during the past decade. WE significantly increases adenoma detection and AI holds the potential to help endoscopists detect more polyps and adenomas. We performed an electronic literature search on PubMed using the following keywords: water-assisted and water exchange colonoscopy, adenoma and polyp detection, artificial intelligence, deep learning, neural networks, and computer-aided colonoscopy. We reviewed relevant articles published in English from 2010 to May 2020. Additional articles were searched manually from the reference lists of the publications reviewed. We discussed recent advances in both WE and AI, including their advantages and limitations. AI may mitigate operator-dependent factors that limit the potential of WE. By increasing bowel cleanliness and improving visualization, WE may provide the platform to optimize the performance of AI for colonoscopies. The strengths of WE and AI may complement each other in spite of their weaknesses to maximize adenoma detection.
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Affiliation(s)
- Chia-Pei Tang
- Division of Gastroenterology, Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan.,School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Paul P Shao
- Sepulveda Ambulatory Care Center, Veterans Affairs Greater Los Angeles Healthcare System, North Hills, CA, USA.,Division of Gastroenterology, Department of Medicine, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA, USA
| | - Yu-Hsi Hsieh
- Division of Gastroenterology, Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan.,School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Felix W Leung
- Sepulveda Ambulatory Care Center, Veterans Affairs Greater Los Angeles Healthcare System, North Hills, CA, USA.,Division of Gastroenterology, Department of Medicine, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA, USA
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45
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Wang P, Liu P, Glissen Brown JR, Berzin TM, Zhou G, Lei S, Liu X, Li L, Xiao X. Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study. Gastroenterology 2020; 159:1252-1261.e5. [PMID: 32562721 DOI: 10.1053/j.gastro.2020.06.023] [Citation(s) in RCA: 118] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 05/10/2020] [Accepted: 06/10/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND AIMS Up to 30% of adenomas might be missed during screening colonoscopy-these could be polyps that appear on-screen but are not recognized by endoscopists or polyps that are in locations that do not appear on the screen at all. Computer-aided detection (CADe) systems, based on deep learning, might reduce rates of missed adenomas by displaying visual alerts that identify precancerous polyps on the endoscopy monitor in real time. We compared adenoma miss rates of CADe colonoscopy vs routine white-light colonoscopy. METHODS We performed a prospective study of patients, 18-75 years old, referred for diagnostic, screening, or surveillance colonoscopies at a single endoscopy center of Sichuan Provincial People's Hospital from June 3, 2019 through September 24, 2019. Same day, tandem colonoscopies were performed for each participant by the same endoscopist. Patients were randomly assigned to groups that received either CADe colonoscopy (n=184) or routine colonoscopy (n=185) first, followed immediately by the other procedure. Endoscopists were blinded to the group each patient was assigned to until immediately before the start of each colonoscopy. Polyps that were missed by the CADe system but detected by endoscopists were classified as missed polyps. False polyps were those continuously traced by the CADe system but then determined not to be polyps by the endoscopists. The primary endpoint was adenoma miss rate, which was defined as the number of adenomas detected in the second-pass colonoscopy divided by the total number of adenomas detected in both passes. RESULTS The adenoma miss rate was significantly lower with CADe colonoscopy (13.89%; 95% CI, 8.24%-19.54%) than with routine colonoscopy (40.00%; 95% CI, 31.23%-48.77%, P<.0001). The polyp miss rate was significantly lower with CADe colonoscopy (12.98%; 95% CI, 9.08%-16.88%) than with routine colonoscopy (45.90%; 95% CI, 39.65%-52.15%) (P<.0001). Adenoma miss rates in ascending, transverse, and descending colon were significantly lower with CADe colonoscopy than with routine colonoscopy (ascending colon 6.67% vs 39.13%; P=.0095; transverse colon 16.33% vs 45.16%; P=.0065; and descending colon 12.50% vs 40.91%, P=.0364). CONCLUSIONS CADe colonoscopy reduced the overall miss rate of adenomas by endoscopists using white-light endoscopy. Routine use of CADe might reduce the incidence of interval colon cancers. chictr.org.cn study no: ChiCTR1900023086.
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Affiliation(s)
- Pu Wang
- Department of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Peixi Liu
- Department of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Jeremy R Glissen Brown
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Guanyu Zhou
- Department of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Shan Lei
- Department of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Xiaogang Liu
- Department of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Liangping Li
- Department of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Xun Xiao
- Department of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
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46
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Lui TKL, Leung WK. Is artificial intelligence the final answer to missed polyps in colonoscopy? World J Gastroenterol 2020; 26:5248-5255. [PMID: 32994685 PMCID: PMC7504252 DOI: 10.3748/wjg.v26.i35.5248] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 06/30/2020] [Accepted: 08/25/2020] [Indexed: 02/06/2023] Open
Abstract
Lesions missed by colonoscopy are one of the main reasons for post-colonoscopy colorectal cancer, which is usually associated with a worse prognosis. Because the adenoma miss rate could be as high as 26%, it has been noted that endoscopists with higher adenoma detection rates are usually associated with lower adenoma miss rates. Artificial intelligence (AI), particularly the deep learning model, is a promising innovation in colonoscopy. Recent studies have shown that AI is not only accurate in colorectal polyp detection but can also reduce the miss rate. Nevertheless, the application of AI in real-time detection has been hindered by heterogeneity of the AI models and study design as well as a lack of long-term outcomes. Herein, we discussed the principle of various AI models and systematically reviewed the current data on the use of AI on colorectal polyp detection and miss rates. The limitations and future prospects of AI on colorectal polyp detection are also discussed.
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Affiliation(s)
- Thomas K L Lui
- Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong, China
| | - Wai K Leung
- Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong, China
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47
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Liu A, Wang H, Lin Y, Fu L, Liu Y, Yan S, Chen H. Gastrointestinal endoscopy nurse assistance during colonoscopy and polyp detection: A PRISMA-compliant meta-analysis of randomized control trials. Medicine (Baltimore) 2020; 99:e21278. [PMID: 32846754 PMCID: PMC7447493 DOI: 10.1097/md.0000000000021278] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Previous studies showed difference results about the effect of nurse in improvement of the colonoscopy detection rate. This meta-analysis aims to investigate whether nurse participation during colonoscopy can help in improving the detection rate of polyps and adenomas. METHODS Original studies in English were searched from the MEDLINE database, PubMed, Web of Science, and the Cochrane Library database. Randomized control trials (RCT) comparing colonoscopy with and without nurse participation for the detection of colorectal polyps and adenomas were identified. A meta-analysis was performed using Revman 5.3 software. RESULTS A total of 2268 patients from 4 RCTs were included in this meta-analysis. Outcomes of colonoscopy with nurse participation were compared with those of colonoscopy without nurse participation. The results showed that nurses' participation during colonoscopy could significantly increase both, polyp detection rate and adenoma detection rate. CONCLUSION Nurse assistance during colonoscopy can help improve the rate of detection of polyps and adenomas.
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Affiliation(s)
- Aihong Liu
- Department of Gastrointestinal Surgery, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou
| | - Huashe Wang
- Department of Gastrointestinal Surgery, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou
| | - Yijia Lin
- Department of Gastrointestinal Surgery, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou
| | - Liping Fu
- Department of Gastrointestinal Endoscopy, The Sixth Affiliated Hospital, Sun Yat-sen University. Guangzhou
| | - Yanan Liu
- Department of Gastrointestinal Endoscopy, The Sixth Affiliated Hospital, Sun Yat-sen University. Guangzhou
| | - Shuhong Yan
- Department of Gastrointestinal Surgery, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou
| | - Honglei Chen
- Gastrointestinal Endoscopy Center, The Eighth Affiliated Hospital, Sun Yat-sen University. Shenzhen, Guangdong, P.R. China
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Effectiveness of a Deep-learning Polyp Detection System in Prospectively Collected Colonoscopy Videos With Variable Bowel Preparation Quality. J Clin Gastroenterol 2020; 54:554-557. [PMID: 31789758 DOI: 10.1097/mcg.0000000000001272] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND Colonoscopy is the gold standard for polyp detection, but polyps may be missed. Artificial intelligence (AI) technologies may assist in polyp detection. To date, most studies for polyp detection have validated algorithms in ideal endoscopic conditions. AIM To evaluate the performance of a deep-learning algorithm for polyp detection in a real-world setting of routine colonoscopy with variable bowel preparation quality. METHODS We performed a prospective, single-center study of 50 consecutive patients referred for colonoscopy. Procedural videos were analyzed by a validated deep-learning AI polyp detection software that labeled suspected polyps. Videos were then re-read by 5 experienced endoscopists to categorize all possible polyps identified by the endoscopist and/or AI, and to measure Boston Bowel Preparation Scale. RESULTS In total, 55 polyps were detected and removed by the endoscopist. The AI system identified 401 possible polyps. A total of 100 (24.9%) were categorized as "definite polyps;" 53/100 were identified and removed by the endoscopist. A total of 63 (15.6%) were categorized as "possible polyps" and were not removed by the endoscopist. In total, 238/401 were categorized as false positives. Two polyps identified by the endoscopist were missed by AI (false negatives). The sensitivity of AI for polyp detection was 98.8%, the positive predictive value was 40.6%. The polyp detection rate for the endoscopist was 62% versus 82% for the AI system. Mean segmental Boston Bowel Preparation Scale were similar (2.64, 2.59, P=0.47) for true and false positives, respectively. CONCLUSIONS A deep-learning algorithm can function effectively to detect polyps in a prospectively collected series of colonoscopies, even in the setting of variable preparation quality.
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Real-time detection of colon polyps during colonoscopy using deep learning: systematic validation with four independent datasets. Sci Rep 2020; 10:8379. [PMID: 32433506 PMCID: PMC7239848 DOI: 10.1038/s41598-020-65387-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 04/28/2020] [Indexed: 01/06/2023] Open
Abstract
We developed and validated a deep-learning algorithm for polyp detection. We used a YOLOv2 to develop the algorithm for automatic polyp detection on 8,075 images (503 polyps). We validated the algorithm using three datasets: A: 1,338 images with 1,349 polyps; B: an open, public CVC-clinic database with 612 polyp images; and C: 7 colonoscopy videos with 26 polyps. To reduce the number of false positives in the video analysis, median filtering was applied. We tested the algorithm performance using 15 unaltered colonoscopy videos (dataset D). For datasets A and B, the per-image polyp detection sensitivity was 96.7% and 90.2%, respectively. For video study (dataset C), the per-image polyp detection sensitivity was 87.7%. False positive rates were 12.5% without a median filter and 6.3% with a median filter with a window size of 13. For dataset D, the sensitivity and false positive rate were 89.3% and 8.3%, respectively. The algorithm detected all 38 polyps that the endoscopists detected and 7 additional polyps. The operation speed was 67.16 frames per second. The automatic polyp detection algorithm exhibited good performance, as evidenced by the high detection sensitivity and rapid processing. Our algorithm may help endoscopists improve polyp detection.
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50
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Aziz M, Weissman S, Khan Z, Fatima R, Lee-Smith W, Nawras A, Adler DG. Use of 2 Observers Increases Adenoma Detection Rate During Colonoscopy: Systematic Review and Meta-analysis. Clin Gastroenterol Hepatol 2020; 18:1240-1242.e3. [PMID: 31589976 DOI: 10.1016/j.cgh.2019.07.033] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 07/16/2019] [Accepted: 07/19/2019] [Indexed: 02/07/2023]
Abstract
Current efforts are directed toward improving quality metrics such as adenoma/polyp detection rates during colonoscopy to decrease the incidence of colorectal cancer.1 Previous studies have reported variable detection rates for adenomas/polyps during colonoscopy for active participation/observation by nurses, trainees, and/or technician (dual observer [DO] group) with an endoscopist.2,3 We performed a systematic review and meta-analysis to evaluate the detection rate of adenomas/polyps during colonoscopy via DO versus single observers (ie, endoscopist alone).
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Affiliation(s)
- Muhammad Aziz
- Department of Internal Medicine, University of Toledo Medical Center, Toledo, Ohio
| | - Simcha Weissman
- Department of Medicine, Hackensack University, Palisades Medical Center, North Bergen, New Jersey
| | - Zubair Khan
- Department of Internal Medicine, University of Toledo Medical Center, Toledo, Ohio
| | - Rawish Fatima
- Department of Internal Medicine, University of Toledo Medical Center, Toledo, Ohio
| | | | - Ali Nawras
- Department of Gastroenterology, University of Toledo, Toledo, Ohio
| | - Douglas G Adler
- Department of Gastroenterology, University of Utah, Salt Lake City, Utah.
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