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Development of a Convolutional Neural Network-Based Colonoscopy Image Assessment Model for Differentiating Crohn’s Disease and Ulcerative Colitis

OBJECTIVE: Evaluation of the endoscopic features of Crohn’s disease (CD) and ulcerative colitis (UC) is the key diagnostic approach in distinguishing these two diseases. However, making diagnostic differentiation of endoscopic images requires precise interpretation by experienced clinicians, which r...

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Autores principales: Wang, Lijia, Chen, Liping, Wang, Xianyuan, Liu, Kaiyuan, Li, Ting, Yu, Yue, Han, Jian, Xing, Shuai, Xu, Jiaxin, Tian, Dean, Seidler, Ursula, Xiao, Fang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9024394/
https://www.ncbi.nlm.nih.gov/pubmed/35463023
http://dx.doi.org/10.3389/fmed.2022.789862
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author Wang, Lijia
Chen, Liping
Wang, Xianyuan
Liu, Kaiyuan
Li, Ting
Yu, Yue
Han, Jian
Xing, Shuai
Xu, Jiaxin
Tian, Dean
Seidler, Ursula
Xiao, Fang
author_facet Wang, Lijia
Chen, Liping
Wang, Xianyuan
Liu, Kaiyuan
Li, Ting
Yu, Yue
Han, Jian
Xing, Shuai
Xu, Jiaxin
Tian, Dean
Seidler, Ursula
Xiao, Fang
author_sort Wang, Lijia
collection PubMed
description OBJECTIVE: Evaluation of the endoscopic features of Crohn’s disease (CD) and ulcerative colitis (UC) is the key diagnostic approach in distinguishing these two diseases. However, making diagnostic differentiation of endoscopic images requires precise interpretation by experienced clinicians, which remains a challenge to date. Therefore, this study aimed to establish a convolutional neural network (CNN)-based model to facilitate the diagnostic classification among CD, UC, and healthy controls based on colonoscopy images. METHODS: A total of 15,330 eligible colonoscopy images from 217 CD patients, 279 UC patients, and 100 healthy subjects recorded in the endoscopic database of Tongji Hospital were retrospectively collected. After selecting the ResNeXt-101 network, it was trained to classify endoscopic images either as CD, UC, or normal. We assessed its performance by comparing the per-image and per-patient parameters of the classification task with that of the six clinicians of different seniority. RESULTS: In per-image analysis, ResNeXt-101 achieved an overall accuracy of 92.04% for the three-category classification task, which was higher than that of the six clinicians (90.67, 78.33, 86.08, 73.66, 58.30, and 86.21%, respectively). ResNeXt-101 also showed higher differential diagnosis accuracy compared with the best performing clinician (CD 92.39 vs. 91.70%; UC 93.35 vs. 92.39%; normal 98.35 vs. 97.26%). In per-patient analysis, the overall accuracy of the CNN model was 90.91%, compared with 93.94, 78.79, 83.33, 59.09, 56.06, and 90.91% of the clinicians, respectively. CONCLUSION: The ResNeXt-101 model, established in our study, performed superior to most clinicians in classifying the colonoscopy images as CD, UC, or healthy subjects, suggesting its potential applications in clinical settings.
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spelling pubmed-90243942022-04-23 Development of a Convolutional Neural Network-Based Colonoscopy Image Assessment Model for Differentiating Crohn’s Disease and Ulcerative Colitis Wang, Lijia Chen, Liping Wang, Xianyuan Liu, Kaiyuan Li, Ting Yu, Yue Han, Jian Xing, Shuai Xu, Jiaxin Tian, Dean Seidler, Ursula Xiao, Fang Front Med (Lausanne) Medicine OBJECTIVE: Evaluation of the endoscopic features of Crohn’s disease (CD) and ulcerative colitis (UC) is the key diagnostic approach in distinguishing these two diseases. However, making diagnostic differentiation of endoscopic images requires precise interpretation by experienced clinicians, which remains a challenge to date. Therefore, this study aimed to establish a convolutional neural network (CNN)-based model to facilitate the diagnostic classification among CD, UC, and healthy controls based on colonoscopy images. METHODS: A total of 15,330 eligible colonoscopy images from 217 CD patients, 279 UC patients, and 100 healthy subjects recorded in the endoscopic database of Tongji Hospital were retrospectively collected. After selecting the ResNeXt-101 network, it was trained to classify endoscopic images either as CD, UC, or normal. We assessed its performance by comparing the per-image and per-patient parameters of the classification task with that of the six clinicians of different seniority. RESULTS: In per-image analysis, ResNeXt-101 achieved an overall accuracy of 92.04% for the three-category classification task, which was higher than that of the six clinicians (90.67, 78.33, 86.08, 73.66, 58.30, and 86.21%, respectively). ResNeXt-101 also showed higher differential diagnosis accuracy compared with the best performing clinician (CD 92.39 vs. 91.70%; UC 93.35 vs. 92.39%; normal 98.35 vs. 97.26%). In per-patient analysis, the overall accuracy of the CNN model was 90.91%, compared with 93.94, 78.79, 83.33, 59.09, 56.06, and 90.91% of the clinicians, respectively. CONCLUSION: The ResNeXt-101 model, established in our study, performed superior to most clinicians in classifying the colonoscopy images as CD, UC, or healthy subjects, suggesting its potential applications in clinical settings. Frontiers Media S.A. 2022-04-08 /pmc/articles/PMC9024394/ /pubmed/35463023 http://dx.doi.org/10.3389/fmed.2022.789862 Text en Copyright © 2022 Wang, Chen, Wang, Liu, Li, Yu, Han, Xing, Xu, Tian, Seidler and Xiao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Wang, Lijia
Chen, Liping
Wang, Xianyuan
Liu, Kaiyuan
Li, Ting
Yu, Yue
Han, Jian
Xing, Shuai
Xu, Jiaxin
Tian, Dean
Seidler, Ursula
Xiao, Fang
Development of a Convolutional Neural Network-Based Colonoscopy Image Assessment Model for Differentiating Crohn’s Disease and Ulcerative Colitis
title Development of a Convolutional Neural Network-Based Colonoscopy Image Assessment Model for Differentiating Crohn’s Disease and Ulcerative Colitis
title_full Development of a Convolutional Neural Network-Based Colonoscopy Image Assessment Model for Differentiating Crohn’s Disease and Ulcerative Colitis
title_fullStr Development of a Convolutional Neural Network-Based Colonoscopy Image Assessment Model for Differentiating Crohn’s Disease and Ulcerative Colitis
title_full_unstemmed Development of a Convolutional Neural Network-Based Colonoscopy Image Assessment Model for Differentiating Crohn’s Disease and Ulcerative Colitis
title_short Development of a Convolutional Neural Network-Based Colonoscopy Image Assessment Model for Differentiating Crohn’s Disease and Ulcerative Colitis
title_sort development of a convolutional neural network-based colonoscopy image assessment model for differentiating crohn’s disease and ulcerative colitis
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9024394/
https://www.ncbi.nlm.nih.gov/pubmed/35463023
http://dx.doi.org/10.3389/fmed.2022.789862
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