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Development and validation of a deep learning-based approach to predict the Mayo endoscopic score of ulcerative colitis

BACKGROUND: The ulcerative colitis (UC) Mayo endoscopy score is a useful tool for evaluating the severity of UC in patients in clinical practice. OBJECTIVES: We aimed to develop and validate a deep learning-based approach to automatically predict the Mayo endoscopic score using UC endoscopic images....

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Autores principales: Qi, Jing, Ruan, Guangcong, Ping, Yi, Xiao, Zhifeng, Liu, Kaijun, Cheng, Yi, Liu, Rongbei, Zhang, Bingqiang, Zhi, Min, Chen, Junrong, Xiao, Fang, Zhao, Tingting, Li, Jiaxing, Zhang, Zhou, Zou, Yuxin, Cao, Qian, Nian, Yongjian, Wei, Yanling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10214058/
https://www.ncbi.nlm.nih.gov/pubmed/37251086
http://dx.doi.org/10.1177/17562848231170945
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author Qi, Jing
Ruan, Guangcong
Ping, Yi
Xiao, Zhifeng
Liu, Kaijun
Cheng, Yi
Liu, Rongbei
Zhang, Bingqiang
Zhi, Min
Chen, Junrong
Xiao, Fang
Zhao, Tingting
Li, Jiaxing
Zhang, Zhou
Zou, Yuxin
Cao, Qian
Nian, Yongjian
Wei, Yanling
author_facet Qi, Jing
Ruan, Guangcong
Ping, Yi
Xiao, Zhifeng
Liu, Kaijun
Cheng, Yi
Liu, Rongbei
Zhang, Bingqiang
Zhi, Min
Chen, Junrong
Xiao, Fang
Zhao, Tingting
Li, Jiaxing
Zhang, Zhou
Zou, Yuxin
Cao, Qian
Nian, Yongjian
Wei, Yanling
author_sort Qi, Jing
collection PubMed
description BACKGROUND: The ulcerative colitis (UC) Mayo endoscopy score is a useful tool for evaluating the severity of UC in patients in clinical practice. OBJECTIVES: We aimed to develop and validate a deep learning-based approach to automatically predict the Mayo endoscopic score using UC endoscopic images. DESIGN: A multicenter, diagnostic retrospective study. METHODS: We collected 15120 colonoscopy images of 768 UC patients from two hospitals in China and developed a deep model based on a vision transformer named the UC-former. The performance of the UC-former was compared with that of six endoscopists on the internal test set. Furthermore, multicenter validation from three hospitals was also carried out to evaluate UC-former’s generalization performance. RESULTS: On the internal test set, the areas under the curve of Mayo 0, Mayo 1, Mayo 2, and Mayo 3 achieved by the UC-former were 0.998, 0.984, 0.973, and 0.990, respectively. The accuracy (ACC) achieved by the UC-former was 90.8%, which is higher than that achieved by the best senior endoscopist. For three multicenter external validations, the ACC was 82.4%, 85.0%, and 83.6%, respectively. CONCLUSIONS: The developed UC-former could achieve high ACC, fidelity, and stability to evaluate the severity of UC, which may provide potential application in clinical practice. REGISTRATION: This clinical trial was registered at the ClinicalTrials.gov (trial registration number: NCT05336773)
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spelling pubmed-102140582023-05-27 Development and validation of a deep learning-based approach to predict the Mayo endoscopic score of ulcerative colitis Qi, Jing Ruan, Guangcong Ping, Yi Xiao, Zhifeng Liu, Kaijun Cheng, Yi Liu, Rongbei Zhang, Bingqiang Zhi, Min Chen, Junrong Xiao, Fang Zhao, Tingting Li, Jiaxing Zhang, Zhou Zou, Yuxin Cao, Qian Nian, Yongjian Wei, Yanling Therap Adv Gastroenterol Original Research BACKGROUND: The ulcerative colitis (UC) Mayo endoscopy score is a useful tool for evaluating the severity of UC in patients in clinical practice. OBJECTIVES: We aimed to develop and validate a deep learning-based approach to automatically predict the Mayo endoscopic score using UC endoscopic images. DESIGN: A multicenter, diagnostic retrospective study. METHODS: We collected 15120 colonoscopy images of 768 UC patients from two hospitals in China and developed a deep model based on a vision transformer named the UC-former. The performance of the UC-former was compared with that of six endoscopists on the internal test set. Furthermore, multicenter validation from three hospitals was also carried out to evaluate UC-former’s generalization performance. RESULTS: On the internal test set, the areas under the curve of Mayo 0, Mayo 1, Mayo 2, and Mayo 3 achieved by the UC-former were 0.998, 0.984, 0.973, and 0.990, respectively. The accuracy (ACC) achieved by the UC-former was 90.8%, which is higher than that achieved by the best senior endoscopist. For three multicenter external validations, the ACC was 82.4%, 85.0%, and 83.6%, respectively. CONCLUSIONS: The developed UC-former could achieve high ACC, fidelity, and stability to evaluate the severity of UC, which may provide potential application in clinical practice. REGISTRATION: This clinical trial was registered at the ClinicalTrials.gov (trial registration number: NCT05336773) SAGE Publications 2023-05-22 /pmc/articles/PMC10214058/ /pubmed/37251086 http://dx.doi.org/10.1177/17562848231170945 Text en © The Author(s), 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research
Qi, Jing
Ruan, Guangcong
Ping, Yi
Xiao, Zhifeng
Liu, Kaijun
Cheng, Yi
Liu, Rongbei
Zhang, Bingqiang
Zhi, Min
Chen, Junrong
Xiao, Fang
Zhao, Tingting
Li, Jiaxing
Zhang, Zhou
Zou, Yuxin
Cao, Qian
Nian, Yongjian
Wei, Yanling
Development and validation of a deep learning-based approach to predict the Mayo endoscopic score of ulcerative colitis
title Development and validation of a deep learning-based approach to predict the Mayo endoscopic score of ulcerative colitis
title_full Development and validation of a deep learning-based approach to predict the Mayo endoscopic score of ulcerative colitis
title_fullStr Development and validation of a deep learning-based approach to predict the Mayo endoscopic score of ulcerative colitis
title_full_unstemmed Development and validation of a deep learning-based approach to predict the Mayo endoscopic score of ulcerative colitis
title_short Development and validation of a deep learning-based approach to predict the Mayo endoscopic score of ulcerative colitis
title_sort development and validation of a deep learning-based approach to predict the mayo endoscopic score of ulcerative colitis
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10214058/
https://www.ncbi.nlm.nih.gov/pubmed/37251086
http://dx.doi.org/10.1177/17562848231170945
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