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 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.

Machine Learning Evaluation of Biliary Atresia Patients to Predict Long-Term Outcome after the Kasai Procedure

“…A similar ML application with the same purpose was made by Angelico et al, who created PopòApp [ 56 ]. Moreover, Zhou et al developed an ensembled deep learning model to facilitate the diagnosis of BA for non-expert radiologists using DB values and US images as well as videos of the gallbladder [ 57 ]. In this setting, this pilot experience is the first that reports an ML evaluation using laboratory and imaging parameters with long-term predictive purposes in patients with BA after KP, supporting the main role of laboratory tests in the follow-up of such patients.…”

Ultrasound for the Diagnosis of Biliary Atresia: From Conventional Ultrasound to Artificial Intelligence

“…Recently, an ensemble deep learning model from US gallbladder images has been developed, which adopted two types of effective AI techniques called deep convolutional neural networks (CNNs) and ensemble learning [ 67 ]. In this study, the training cohort was randomly separated into five complementary subsets, four of which were used each time to train a CNN, and the remaining subset was used for validation.…”

A systematic review on application of deep learning in digestive system image processing

“…A prospective five-center study using DL from ultrasound videos of biliary atresia achieved higher diagnostic accuracy than human experts. The research team has also developed a mobile APP by DL of ultrasound pictures, enabling rural doctors in remote areas to perform CAD by taking and uploading photographs of suspected biliary atresia [ 66 ].…”