Yuichi Mori1, Shin-Ei Kudo2, James E East3, Amit Rastogi4, Michael Bretthauer5, Masashi Misawa1, Masau Sekiguchi6, Takahisa Matsuda6, Yutaka Saito7, Hiroaki Ikematsu8, Kinichi Hotta9, Kazuo Ohtsuka10, Toyoki Kudo1, Kensaku Mori11. 1. Department of Health Management and Health Economics, Institute of Health and Society, University of Oslo, Oslo, Norway. 2. Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan. 3. Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford, United Kingdom; Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, United Kingdom. 4. Division of Gastroenterology, University of Kansas Medical Center, Kansas City, Kansas, USA. 5. Department of Health Management and Health Economics, Institute of Health and Society, University of Oslo, Oslo, Norway; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway. 6. Cancer Screening Center, National Cancer Center Hospital, Tokyo, Japan; Division of Screening Technology, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan; Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan. 7. Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan. 8. Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan. 9. Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan. 10. Department of Endoscopy, Tokyo Medical and Dental University, Tokyo, Japan. 11. Graduate School of Informatics, Nagoya University, Nagoya, Japan.
Abstract
BACKGROUND AND AIMS: Artificial intelligence (AI) is being implemented in colonoscopy practice, but no study has investigated whether AI is cost saving. We aimed to quantify the cost reduction using AI as an aid in the optical diagnosis of colorectal polyps. METHODS: This study is an add-on analysis of a clinical trial that investigated the performance of AI for differentiating colorectal polyps (ie, neoplastic versus non-neoplastic). We included all patients with diminutive (≤5 mm) rectosigmoid polyps in the analyses. The average colonoscopy cost was compared for 2 scenarios: (1) a diagnose-and-leave strategy supported by the AI prediction (ie, diminutive rectosigmoid polyps were not removed when predicted as non-neoplastic), and (2) a resect-all-polyps strategy. Gross annual costs for colonoscopies were also calculated based on the number and reimbursement of colonoscopies conducted under public health insurances in 4 countries. RESULTS: Overall, 207 patients with 250 diminutive rectosigmoid polyps (104 neoplastic, 144 non-neoplastic, and 2 indeterminate) were included. AI correctly differentiated neoplastic polyps with 93.3% sensitivity, 95.2% specificity, and 95.2% negative predictive value. Thus, 105 polyps were removed and 145 were left under the diagnose-and-leave strategy, which was estimated to reduce the average colonoscopy cost and the gross annual reimbursement for colonoscopies by 18.9% and US$149.2 million in Japan, 6.9% and US$12.3 million in England, 7.6% and US$1.1 million in Norway, and 10.9% and US$85.2 million in the United States, respectively, compared with the resect-all-polyps strategy. CONCLUSIONS: The use of AI to enable the diagnose-and-leave strategy results in substantial cost reductions for colonoscopy.
BACKGROUND AND AIMS: Artificial intelligence (AI) is being implemented in colonoscopy practice, but no study has investigated whether AI is cost saving. We aimed to quantify the cost reduction using AI as an aid in the optical diagnosis of colorectal polyps. METHODS: This study is an add-on analysis of a clinical trial that investigated the performance of AI for differentiating colorectal polyps (ie, neoplastic versus non-neoplastic). We included all patients with diminutive (≤5 mm) rectosigmoid polyps in the analyses. The average colonoscopy cost was compared for 2 scenarios: (1) a diagnose-and-leave strategy supported by the AI prediction (ie, diminutive rectosigmoid polyps were not removed when predicted as non-neoplastic), and (2) a resect-all-polyps strategy. Gross annual costs for colonoscopies were also calculated based on the number and reimbursement of colonoscopies conducted under public health insurances in 4 countries. RESULTS: Overall, 207 patients with 250 diminutive rectosigmoid polyps (104 neoplastic, 144 non-neoplastic, and 2 indeterminate) were included. AI correctly differentiated neoplastic polyps with 93.3% sensitivity, 95.2% specificity, and 95.2% negative predictive value. Thus, 105 polyps were removed and 145 were left under the diagnose-and-leave strategy, which was estimated to reduce the average colonoscopy cost and the gross annual reimbursement for colonoscopies by 18.9% and US$149.2 million in Japan, 6.9% and US$12.3 million in England, 7.6% and US$1.1 million in Norway, and 10.9% and US$85.2 million in the United States, respectively, compared with the resect-all-polyps strategy. CONCLUSIONS: The use of AI to enable the diagnose-and-leave strategy results in substantial cost reductions for colonoscopy.
Authors: Rahul Pannala; Kumar Krishnan; Joshua Melson; Mansour A Parsi; Allison R Schulman; Shelby Sullivan; Guru Trikudanathan; Arvind J Trindade; Rabindra R Watson; John T Maple; David R Lichtenstein Journal: VideoGIE Date: 2020-11-09
Authors: Omer F Ahmad; Yuichi Mori; Masashi Misawa; Shin-Ei Kudo; John T Anderson; Jorge Bernal; Tyler M Berzin; Raf Bisschops; Michael F Byrne; Peng-Jen Chen; James E East; Tom Eelbode; Daniel S Elson; Suryakanth R Gurudu; Aymeric Histace; William E Karnes; Alessandro Repici; Rajvinder Singh; Pietro Valdastri; Michael B Wallace; Pu Wang; Danail Stoyanov; Laurence B Lovat Journal: Endoscopy Date: 2021-01-13 Impact factor: 9.776
Authors: Alison L Antes; Sara Burrous; Bryan A Sisk; Matthew J Schuelke; Jason D Keune; James M DuBois Journal: BMC Med Inform Decis Mak Date: 2021-07-20 Impact factor: 2.796