Clinical Analysis of Deep Learning Technology in Assisting Diagnosis of Colorectal Polyps
10.3969/j.issn.1008-7125.2020.07.002
- Author:
Lianghui JIANG
1
;
Rongqiu ZHANG
1
;
Xinying MENG
1
;
Changhong ZHOU
1
;
Xin SUN
2
;
Xuetong LI
3
Author Information
1. Department of Health Care, Qingdao Municipal Hospital (East) Affiliated to Qingdao University
2. Department of Endoscopy Center, Qingdao Municipal Hospital (East) Affiliated to Qingdao University
3. Sino-German Joint Software Institute, Beihang University
- Publication Type:Journal Article
- Keywords:
Artificial Intelligence;
Deep Learning;
Diagnosis;
Intestinal Polyps
- From:
Chinese Journal of Gastroenterology
2020;25(7):389-394
- CountryChina
- Language:Chinese
-
Abstract:
Background: Computer-aided diagnosis based on deep learning technology is a research hotspot in the field of gastroenterology, and computer-aided diagnosis of colorectal polyps has received more and more attention. Aims: To validate a model based on deep learning for the automatic identification of colorectal polyps, and to analyze its auxiliary learning function for helping novice endoscopists. Methods: A total of 1 200 colonoscopy images (600 colorectal polyp images and 600 normal images) in the endoscopy center database of Qingdao Municipal Hospital (East) from January 2019 to January 2020 were retrospectively collected. Deep learning model was used to identify the 1 200 images. The sensitivity, specificity, accuracy and diagnosis time of deep learning model and 5 novice endoscopists for diagnosis of colorectal polyps were compared. Results: The deep learning model showed a sensitivity of 93.2%, specificity of 98.7%, accuracy of 95.9% for detecting colorectal polyps, and the diagnosis time of each image was (0.20±0.03) second. The sensitivity, accuracy, and diagnosis time of the model were superior to 5 novice endoscopists, and the specificity was superior to some novice endoscopists. The accuracies of model for polyps with size ≤5 mm and 6~9 mm were 88.1% and 96.8%, respectively, and were superior to 5 novice endoscopists; the accuracy of model for polyps with size ≥10 mm was 100%, and was similar to 5 novice endoscopists. The accuracy of model for polyps with protrude type was 94.8%, and was superior to some novice endoscopists; the accuracy of model for polyps with flat type was 91.7%, and was superior to 5 novice endoscopists. Missing the polyps with flat type (38.8%), polyps at mucosal folds (32.7%), and mistaking the mucosal folds as polyps (12.2%) were the main causes of false negative or false positive results of the model. Conclusions: The deep learning model has a high accuracy, sensitivity, specificity and shorter diagnosis time for diagnosis of colorectal polyps, and can be used to assist novice endoscopists in diagnosing small polyps and flat polyps.
