Pilot study of artificial intelligence ultrasound diagnosis of biliary atresia based on deep learning
10.3760/cma.j.cn131148-20201204-00917
- VernacularTitle:基于深度学习的胆道闭锁超声智能诊断的初步研究
- Author:
Liu YANG
;
Chun DU
;
Wenjuan CHEN
;
Xingxing DUAN
;
Mengjie ZHOU
;
Huan WEN
- From:
Chinese Journal of Ultrasonography
2021;30(5):402-407
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the feasibility of artificial intelligence ultrasound to diagnose of biliary atresia (BA) based on deep learning.Methods:A total of 531 gallbladder ultrasound images in 177 cases of BA patients (BA group) and 585 gallbladder ultrasound images in 195 cases of Non-BA patients (Non-BA group) were collected in Hunan Children′s Hospital from September 2018 to October 2020. For the BA and Non-BA groups, all images were divided into training set and test set according to the ratio of 2∶1. The Mask R-CNN model was trained by training samples, and then the model was tested, according to patient and image as a unit respectively, to evaluate the gallbladder organ detection rate and the diagnostic accuracy of BA. In addition, the images of the test set were randomly numbered.Four sonographers were invited to interpret the images to calculate the diagnostic accuracy individually. Last, the diagnostic accuracy of the Mask R-CNN model was compared with that of sonographers.Results:In terms of the automatic detection of gallbladder organs, the detection rate in both BA and Non-BA group reached 100%, but there were 17 false alarms in 372 test images, with a false alarm rate of 4.57%. In terms of the diagnostic rate of gallbladders, when taking patient as a unit, the total diagnostic accuracy of the model in the test set was 95.97%, which was higher than that of the sonographers in other hospitals and the sonographer with intermediate professional title in our hospital, and the difference was statistically significant ( P<0.005). It was slightly higher than that of sonographer with senior professional title in our hospital (91.94%), but the difference was not statistically significant ( P=0.183). When taking picture as a unit, the total diagnostic accuracy of the model was 97.04%, which was higher than that of the sonographers in other hospitals and the sonographer with intermediate professional title in our hospital, and the difference was statistically significant ( P<0.001). It was slightly higher than that of sonographer with senior professional title in our hospital (94.09%), but the difference was not statistically significant ( P=0.05). Conclusions:The artificial intelligence technology based on Mask R-CNN can automatically and accurately detect gallbladder organs and diagnose BA, which is worthy of further study.
