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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.

It is still difficult to make accurate diagnosis of biliary atresia (BA) by sonographic gallbladder images particularly in rural area lacking relevant expertise. To provide an artificial intelligence solution to help diagnose BA based on sonographic gallbladder images, an ensembled deep learning model was developed based on a small set of sonographic images. The model yielded a patient-level sensitivity 93.1% and specificity 93.9% (with AUROC 0.956) on the multi-center external validation dataset, superior to that of human experts. With the help of the model, the performance of human experts with various levels would be improved further. Moreover, the diagnosis based on smartphone photos of sonographic gallbladder images through a smartphone app and based on video sequences by the model still yielded expert-level performance. Our study provides a deep learning solution to help radiologists improve BA diagnosis in various clinical application scenarios, particularly in rural and undeveloped regions with limited expertise.

Purpose. To investigate the diagnostic performance of shear wave elastography (SWE) for measuring liver stiffness to identify and differentiate biliary atresia (BA) from cholestatic hepatitis in infants younger than 90 days. Methods. A total of 138 infants younger than 90 days with cholestatic hepatitis were examined by SWE. The infants were subclassified into BA and nonbiliary atresia (non-BA) groups. Receiver operating characteristic (ROC) analysis was used to determine the sensitivity and specificity of hepatic Young’s modulus measurements, the ultrasonic findings in the differential diagnosis of suspected BA, and the cut-off value to diagnose BA. Results. In all infants with cholestatic hepatitis, the cut-off value of hepatic Young’s modulus to differentiate the BA group from the non-BA group was 12.35 kPa and the area under the ROC curve (AUC) was 0.937, with a sensitivity of 84.3% and a specificity of 89.7%; nevertheless the AUC of the abnormal gallbladder (AbGB) was 0.940, with a sensitivity of 96.1% and a specificity of 92.0%. In the parallel test, triangular cord (TC) sign combined with AbGB had the best diagnostic performance and the AUC was 0.960, with a sensitivity of 100% and a specificity of 92.0%. In the serial test, SWE combined with AbGB achieved the best diagnostic performance; the AUC was 0.902, the sensitivity and specificity were 80.4% and 100%, respectively. Conclusions. SWE could not only help differentiate BA from cholestatic hepatic diseases but also increase the diagnostic specificity when combined with grey-scale ultrasound in the serial test.