Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images

Wenying Zhou, Yang Yang, Cheng Yu, Juxian Liu, Xingxing Duan, Zongjie Weng, Dan Chen, Qianhong Liang, Qin Fang, Jiaojiao Zhou, Hao Ju, Zhenhua Luo, Weihao Guo, Xiaoyan Ma, Xiaoyan Xie (), Ruixuan Wang () and Luyao Zhou ()
Additional contact information
Wenying Zhou: Sun Yat-sen University
Yang Yang: Sun Yat-sen University
Cheng Yu: Huazhong University of Science and Technology
Juxian Liu: Sichuan University
Xingxing Duan: Hunan Children’s Hospital
Zongjie Weng: Affiliated Hospital of Fujian Medical University
Dan Chen: Guangdong Women and Children’ Hospital
Qianhong Liang: Hexian Memorial Affiliated Hospital of Southern Medical University
Qin Fang: The First People’s Hospital of Foshan
Jiaojiao Zhou: Sichuan University
Hao Ju: Shengjing Hospital of China Medical University
Zhenhua Luo: Sun Yat-sen University
Weihao Guo: Sun Yat-sen University
Xiaoyan Ma: Guangdong Women and Children’ Hospital
Xiaoyan Xie: Sun Yat-sen University
Ruixuan Wang: Sun Yat-sen University
Luyao Zhou: Sun Yat-sen University

Nature Communications, 2021, vol. 12, issue 1, 1-14

Abstract: Abstract It is still challenging to make accurate diagnosis of biliary atresia (BA) with sonographic gallbladder images particularly in rural area without relevant expertise. To help diagnose BA based on sonographic gallbladder images, an ensembled deep learning model is developed. The model yields a patient-level sensitivity 93.1% and specificity 93.9% [with areas under the receiver operating characteristic curve of 0.956 (95% confidence interval: 0.928-0.977)] on the multi-center external validation dataset, superior to that of human experts. With the help of the model, the performances of human experts with various levels are improved. Moreover, the diagnosis based on smartphone photos of sonographic gallbladder images through a smartphone app and based on video sequences by the model still yields expert-level performances. The ensembled deep learning model in this study provides a solution to help radiologists improve the diagnosis of BA in various clinical application scenarios, particularly in rural and undeveloped regions with limited expertise.

Date: 2021
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DOI: 10.1038/s41467-021-21466-z

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