{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T14:54:46Z","timestamp":1774364086361,"version":"3.50.1"},"reference-count":15,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82570629"],"award-info":[{"award-number":["82570629"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82270569"],"award-info":[{"award-number":["82270569"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Explorers Program of Shanghai","award":["25TS1411400"],"award-info":[{"award-number":["25TS1411400"]}]},{"name":"Shanghai  Science and Technology Industry High-Quality Development Program","award":["25SF1905700"],"award-info":[{"award-number":["25SF1905700"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Background and Aims: Adequate bowel preparation is the cornerstone of high-quality colonoscopy. The Boston Bowel Preparation Scale (BBPS) is the gold standard for assessment, yet its application suffers from inter-observer variability and lacks a fully automated solution for entire video analysis. This study proposes EndoClean, a novel, fully automated deep learning framework designed to compute the full-segment BBPS score from colonoscopy videos, aiming to provide a standardized, objective, and near expert-level assessment. Methods: EndoClean integrates three distinct models: frame selection, anatomical segmentation, and BBPS scoring. Its performance was rigorously evaluated against a reference standard established by senior experts and compared with junior endoscopists. We assessed assessment precision, inter-rater agreement (quadratic weighted Kappa), and consistency across all colonic segments. Results: The EndoClean system demonstrated superior reliability, achieving a global accuracy of 97.8% for the total BBPS score, with satisfying agreement with senior experts (\u03ba = 0.984; 95% CI: 0.976\u20130.989). Notably, EndoClean performed significantly better than junior endoscopists in overall BBPS agreements (\u03ba: 0.984 vs. 0.949, p &lt; 0.001) and overall accuracy (97.8% vs. 94.6%, p = 0.037). In segment-specific analysis, the EndoClean surpassed junior doctors particularly in the transverse colon (Accuracy: 97.5% vs. 90.4%, p &lt; 0.001) and effectively reduced misclassifications in clinically ambiguous intermediate scores. For binary adequacy classification, the system achieved a sensitivity of 98.2% and a specificity of 97.3%. Conclusions: EndoClean represents a robust solution in automated quality control, demonstrating performance comparable to senior experts in bowel preparation assessment. By significantly reducing the variability seen in junior endoscopists and providing objective, full-video BBPS scoring, this framework offers a viable, standardized, and efficient solution for clinical practice and large-scale quality monitoring.<\/jats:p>","DOI":"10.3390\/bioengineering13030294","type":"journal-article","created":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T14:55:01Z","timestamp":1772463301000},"page":"294","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EndoClean: A Hybrid Deep Learning Framework for Automated Full-Video Boston Bowel Preparation Scale Assessment"],"prefix":"10.3390","volume":"13","author":[{"given":"Yan","family":"Zhu","sequence":"first","affiliation":[{"name":"Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China"},{"name":"Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China"},{"name":"Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai 200032, China"},{"name":"Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8698-7685","authenticated-orcid":false,"given":"Si-Yuan","family":"Li","sequence":"additional","affiliation":[{"name":"Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China"},{"name":"Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China"},{"name":"Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai 200032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pei-Yao","family":"Fu","sequence":"additional","affiliation":[{"name":"Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China"},{"name":"Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China"},{"name":"Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2947-8783","authenticated-orcid":false,"given":"Shuo","family":"Wang","sequence":"additional","affiliation":[{"name":"Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China"},{"name":"Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China"},{"name":"Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai 200032, China"},{"name":"Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China"},{"name":"Data Science Institute, Imperial College London, London SW7 2AZ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan-Lin","family":"Li","sequence":"additional","affiliation":[{"name":"Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China"},{"name":"Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5434-0540","authenticated-orcid":false,"given":"Ping-Hong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China"},{"name":"Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"89","DOI":"10.5114\/pg.2018.81072","article-title":"Epidemiology of colorectal cancer: Incidence, mortality, survival, and risk factors","volume":"14","author":"Prashanth","year":"2019","journal-title":"Gastroenterol. 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