Development of an artificial intelligence-based multimodal diagnostic system for early detection of biliary atresia.

ABSTRACT:

Background

Early diagnosis of biliary atresia (BA) is crucial for improving patient outcomes, yet remains a significant global challenge. This challenge may be ameliorated through the application of artificial intelligence (AI). Despite the promise of AI in medical diagnostics, its application to multimodal BA data has not yet achieved substantial breakthroughs. This study aims to leverage diverse data sources and formats to develop an intelligent diagnostic system for BA.

Methods

We constructed the largest known multimodal BA dataset, comprising ultrasound images, clinical data, and laboratory results. Using this dataset, we developed a novel deep learning model and simplified it using easily obtainable data, eliminating the need for blood samples. The models were externall

SUBMITTER: Ma Y 

PROVIDER: S-EPMC11866655 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature