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

作者：Zhou, Wenying; Yang, Yang; Yu, Cheng; Liu, Juxian; Duan, Xingxing; Weng, Zongjie; Chen, Dan; Liang, Qianhong; Fang, Qin; Zhou, Jiaojiao; Ju, Hao; Luo, Zhenhua; Guo, Weihao; Ma, Xiaoyan; Xie, Xiaoyan^*; Wang, Ruixuan^*; Zhou, Luyao^*

来源：Nature Communications, 2021, 12(1): 1259.

DOI：10.1038/s41467-021-21466-z

• TRIANGULAR CORD SIGN
• US
• EPIDEMIOLOGY
• ULTRASOUND
• FREQUENCY

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. It is still challenging to make accurate diagnosis of biliary atresia (BA) with sonographic gallbladder images particularly in rural areas without relevant expertise. Here, the authors develop a diagnostic deep learning model which favourable performance in comparison with human experts in multi-center external validation.

• 单位
  华中科技大学; 中山大学; 中国医科大学; 1; 四川大学; 南方医科大学; 佛山市第一人民医院