Application of deep learning to the differenciation of the invasion depth in colorectal adenomas
10.3760/cma.j.cn321463-20221130-00751
- VernacularTitle:深度学习技术在辅助结直肠腺瘤浸润深度鉴别中的应用研究
- Author:
Youming XU
1
;
Liwen YAO
;
Zihua LU
;
Honggang YU
Author Information
1. 武汉大学人民医院消化内科 消化疾病湖北省重点实验室，武汉 430060
- Keywords:
Colorectal neoplasms;
Machine learning;
Artificial intelligence;
Tumor infiltrating;
Diagnosis, computer-assisted
- From:
Chinese Journal of Digestive Endoscopy
2023;40(7):534-538
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To evaluate deep learning for differentiating invasion depth of colorectal adenomas under image enhanced endoscopy (IEE).Methods:A total of 13 246 IEE images from 3 714 lesions acquired from November 2016 to June 2021 were retrospectively collected in Renmin Hospital of Wuhan University, Shenzhen Hospital of Southern Medical University and the First Hospital of Yichang to construct a deep learning model to differentiate submucosal deep invasion and non-submucosal deep invasion lesions of colorectal adenomas. The performance of the deep learning model was validated in an independent test and an external test. The full test was used to compare the diagnostic performance between 5 endoscopists and the deep learning model. A total of 35 videos were collected from January to June 2021 in Renmin Hospital of Wuhan University to validate the diagnostic performance of the endoscopists with the assistance of deep learning model.Results:The accuracy and Youden index of the deep learning model in image test set were 93.08% (821/882) and 0.86, which were better than those of endoscopists [the highest were 91.72% (809/882) and 0.78]. In video test set, the accuracy and Youden index of the model were 97.14% (34/35) and 0.94. With the assistance of the model, the accuracy of endoscopists was significantly improved [the highest was 97.14% (34/35)].Conclusion:The deep learning model obtained in this study could identify submucosal lesions with deep invasion accurately for colorectal adenomas, and could improve the diagnostic accuracy of endoscopists.
