Deep learning for the improvement of the accuracy of colorectal polyp classification
10.3760/cma.j.cn321463-20210109-00018
- VernacularTitle:深度学习技术在提升结直肠息肉性质鉴别准确率中的应用
- Author:
Dexin GONG
1
;
Jun ZHANG
;
Wei ZHOU
;
Lianlian WU
;
Shan HU
;
Honggang YU
Author Information
1. 武汉大学人民医院消化内科 430060
- Keywords:
Machine learning;
Adenomatous polyps;
Colorectal neoplasms;
Narrow band imaging
- From:
Chinese Journal of Digestive Endoscopy
2021;38(10):801-805
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To evaluate deep learning in improving the diagnostic rate of adenomatous and non-adenomatous polyps.Methods:Non-magnifying narrow band imaging (NBI) polyp images obtained from Endoscopy Center of Renmin Hospital, Wuhan University were divided into three datasets. Dataset 1 (2 699 adenomatous and 1 846 non-adenomatous non-magnifying NBI polyp images from January 2018 to October 2020) was used for model training and validation of the diagnosis system. Dataset 2 (288 adenomatous and 210 non-adenomatous non-magnifying NBI polyp images from January 2018 to October 2020) was used to compare the accuracy of polyp classification between the system and endoscopists. At the same time, the accuracy of 4 trainees in polyp classification with and without the assistance of this system was compared. Dataset 3 (203 adenomatous and 141 non-adenomatous non-magnifying NBI polyp images from November 2020 to January 2021) was used to prospectively test the system.Results:The accuracy of the system in polyp classification was 90.16% (449/498) in dataset 2, superior to that of endoscopists. With the assistance of the system, the accuracy of colorectal polyp diagnosis was significantly improved. In the prospective study, the accuracy of the system was 89.53% (308/344).Conclusion:The colorectal polyp classification system based on deep learning can significantly improve the accuracy of trainees in polyp classification.
