Influence of artificial intelligence on endoscopists′ performance in diagnosing gastric cancer by magnifying narrow banding imaging
10.3760/cma.j.cn321463-20210110-00020
- VernacularTitle:人工智能对内镜医师染色放大内镜下胃癌识别能力的影响研究
- Author:
Jing WANG
1
;
Yijie ZHU
;
Lianlian WU
;
Xinqi HE
;
Zehua DONG
;
Manling HUANG
;
Yisi CHEN
;
Meng LIU
;
Qinghong XU
;
Honggang YU
;
Qi WU
Author Information
1. 北京大学肿瘤医院暨北京市肿瘤防治研究所内镜中心 恶性肿瘤发病机制及转化研究教育部重点实验室 100142
- Keywords:
Artificial intelligence;
Gastric cancer;
Narrow banding imaging
- From:
Chinese Journal of Digestive Endoscopy
2021;38(10):783-788
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To assess the influence of an artificial intelligence (AI) -assisted diagnosis system on the performance of endoscopists in diagnosing gastric cancer by magnifying narrow banding imaging (M-NBI).Methods:M-NBI images of early gastric cancer (EGC) and non-gastric cancer from Renmin Hospital of Wuhan University from March 2017 to January 2020 and public datasets were collected, among which 4 667 images (1 950 images of EGC and 2 717 of non-gastric cancer)were included in the training set and 1 539 images (483 images of EGC and 1 056 of non-gastric cancer) composed a test set. The model was trained using deep learning technique. One hundred M-NBI videos from Beijing Cancer Hospital and Renmin Hospital of Wuhan University between 9 June 2020 and 17 November 2020 were prospectively collected as a video test set, 38 of gastric cancer and 62 of non-gastric cancer. Four endoscopists from four other hospitals participated in the study, diagnosing the video test twice, with and without AI. The influence of the system on endoscopists′ performance was assessed.Results:Without AI assistance, accuracy, sensitivity, and specificity of endoscopists′ diagnosis of gastric cancer were 81.00%±4.30%, 71.05%±9.67%, and 87.10%±10.88%, respectively. With AI assistance, accuracy, sensitivity and specificity of diagnosis were 86.50%±2.06%, 84.87%±11.07%, and 87.50%±4.47%, respectively. Diagnostic accuracy ( P=0.302) and sensitivity ( P=0.180) of endoscopists with AI assistance were improved compared with those without. Accuracy, sensitivity and specificity of AI in identifying gastric cancer in the video test set were 88.00% (88/100), 97.37% (37/38), and 82.26% (51/62), respectively. Sensitivity of AI was higher than that of the average of endoscopists ( P=0.002). Conclusion:AI-assisted diagnosis system is an effective tool to assist diagnosis of gastric cancer in M-NBI, which can improve the diagnostic ability of endoscopists. It can also remind endoscopists of high-risk areas in real time to reduce the probability of missed diagnosis.
