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

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 yiel

SUBMITTER: Zhou W 

PROVIDER: S-EPMC7904842 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature