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Artificial intelligence-based assessments of colonoscopic withdrawal technique: a new method for measuring and enhancing the quality of fold examination
Background This study aimed to develop an artificial intelligence (AI)-based system for measuring fold examination quality (FEQ) of colonoscopic withdrawal technique. We also examined the relationship between the system’s evaluation of FEQ and FEQ scores from experts, and adenoma detection rate (AD...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Georg Thieme Verlag KG
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9500011/ https://www.ncbi.nlm.nih.gov/pubmed/35391493 http://dx.doi.org/10.1055/a-1799-8297 |
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author | Liu, Wei Wu, Yu Yuan, Xianglei Zhang, Jingyu Zhou, Yao Zhang, Wanhong Zhu, Peipei Tao, Zhang He, Long Hu, Bing Yi, Zhang |
author_facet | Liu, Wei Wu, Yu Yuan, Xianglei Zhang, Jingyu Zhou, Yao Zhang, Wanhong Zhu, Peipei Tao, Zhang He, Long Hu, Bing Yi, Zhang |
author_sort | Liu, Wei |
collection | PubMed |
description | Background This study aimed to develop an artificial intelligence (AI)-based system for measuring fold examination quality (FEQ) of colonoscopic withdrawal technique. We also examined the relationship between the system’s evaluation of FEQ and FEQ scores from experts, and adenoma detection rate (ADR) and withdrawal time of colonoscopists, and evaluated the system’s ability to improve FEQ during colonoscopy. Methods First, we developed an AI-based system for measuring FEQ. Next, 103 consecutive colonoscopies performed by 11 colonoscopists were collected for evaluation. Three experts graded FEQ of each colonoscopy, after which the recorded colonoscopies were evaluated by the system. We further assessed the system by correlating its evaluation of FEQ against expert scoring, historical ADR, and withdrawal time of each colonoscopist. We also conducted a prospective observational study to evaluate the systemʼs performance in enhancing fold examination. Results The system’s evaluations of FEQ of each endoscopist were significantly correlated with expertsʼ scores (r = 0.871, P < 0.001), historical ADR (r = 0.852, P = 0.001), and withdrawal time (r = 0.727, P = 0.01). For colonoscopies performed by colonoscopists with previously low ADRs (< 25 %), AI assistance significantly improved the FEQ, evaluated by both the AI system (0.29 [interquartile range (IQR) 0.27–0.30] vs. 0.23 [0.17–0.26]) and experts (14.00 [14.00–15.00] vs. 11.67 [10.00–13.33]) (both P < 0.001). Conclusion The system’s evaluation of FEQ was strongly correlated with FEQ scores from experts, historical ADR, and withdrawal time of each colonoscopist. The system has the potential to enhance FEQ. |
format | Online Article Text |
id | pubmed-9500011 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Georg Thieme Verlag KG |
record_format | MEDLINE/PubMed |
spelling | pubmed-95000112022-09-24 Artificial intelligence-based assessments of colonoscopic withdrawal technique: a new method for measuring and enhancing the quality of fold examination Liu, Wei Wu, Yu Yuan, Xianglei Zhang, Jingyu Zhou, Yao Zhang, Wanhong Zhu, Peipei Tao, Zhang He, Long Hu, Bing Yi, Zhang Endoscopy Background This study aimed to develop an artificial intelligence (AI)-based system for measuring fold examination quality (FEQ) of colonoscopic withdrawal technique. We also examined the relationship between the system’s evaluation of FEQ and FEQ scores from experts, and adenoma detection rate (ADR) and withdrawal time of colonoscopists, and evaluated the system’s ability to improve FEQ during colonoscopy. Methods First, we developed an AI-based system for measuring FEQ. Next, 103 consecutive colonoscopies performed by 11 colonoscopists were collected for evaluation. Three experts graded FEQ of each colonoscopy, after which the recorded colonoscopies were evaluated by the system. We further assessed the system by correlating its evaluation of FEQ against expert scoring, historical ADR, and withdrawal time of each colonoscopist. We also conducted a prospective observational study to evaluate the systemʼs performance in enhancing fold examination. Results The system’s evaluations of FEQ of each endoscopist were significantly correlated with expertsʼ scores (r = 0.871, P < 0.001), historical ADR (r = 0.852, P = 0.001), and withdrawal time (r = 0.727, P = 0.01). For colonoscopies performed by colonoscopists with previously low ADRs (< 25 %), AI assistance significantly improved the FEQ, evaluated by both the AI system (0.29 [interquartile range (IQR) 0.27–0.30] vs. 0.23 [0.17–0.26]) and experts (14.00 [14.00–15.00] vs. 11.67 [10.00–13.33]) (both P < 0.001). Conclusion The system’s evaluation of FEQ was strongly correlated with FEQ scores from experts, historical ADR, and withdrawal time of each colonoscopist. The system has the potential to enhance FEQ. Georg Thieme Verlag KG 2022-04-07 /pmc/articles/PMC9500011/ /pubmed/35391493 http://dx.doi.org/10.1055/a-1799-8297 Text en The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited. |
spellingShingle | Liu, Wei Wu, Yu Yuan, Xianglei Zhang, Jingyu Zhou, Yao Zhang, Wanhong Zhu, Peipei Tao, Zhang He, Long Hu, Bing Yi, Zhang Artificial intelligence-based assessments of colonoscopic withdrawal technique: a new method for measuring and enhancing the quality of fold examination |
title | Artificial intelligence-based assessments of colonoscopic withdrawal technique: a new method for measuring and enhancing the quality of fold examination |
title_full | Artificial intelligence-based assessments of colonoscopic withdrawal technique: a new method for measuring and enhancing the quality of fold examination |
title_fullStr | Artificial intelligence-based assessments of colonoscopic withdrawal technique: a new method for measuring and enhancing the quality of fold examination |
title_full_unstemmed | Artificial intelligence-based assessments of colonoscopic withdrawal technique: a new method for measuring and enhancing the quality of fold examination |
title_short | Artificial intelligence-based assessments of colonoscopic withdrawal technique: a new method for measuring and enhancing the quality of fold examination |
title_sort | artificial intelligence-based assessments of colonoscopic withdrawal technique: a new method for measuring and enhancing the quality of fold examination |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9500011/ https://www.ncbi.nlm.nih.gov/pubmed/35391493 http://dx.doi.org/10.1055/a-1799-8297 |
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