Transfer learning method for prenatal ultrasound diagnosis of biliary atresia

作者：Fujiao He, Gang Li, Zhichao Zhang, Chaoran Yang, Zeyu Yang, Hao Ding, Dan Zhao, Wei Sun, Yu Wang, Kaihui Zeng, Li X, Ming‐Ming Shao, Jiao Yin, Jiaxi Yao, Boxuan Hong, Zhibo Zhang, Zhengwei Yuan, Zongjie Weng, Luyao Zhou, Mo Zhang, Lizhu Chen · 发表于：npj Digital Medicine · 年份：2025 · DOI：10.1038/s41746-025-01525-1 · 被引用次数：4 · 研究领域：Pediatric Hepatobiliary Diseases and Treatments、Fetal and Pediatric Neurological Disorders、Domain Adaptation and Few-Shot Learning

Biliary atresia (BA) is a rare and severe congenital disorder with a significant challenge for prenatal diagnosis. This study, registered at the Chinese Clinical Trial Registry (ChiCTR2200059705), aimed to develop an intelligent model to aid in the prenatal diagnosis of BA. To develop and evaluate this model, fetuses from 20 hospitals across China and infants sourced from public database were collected. The transfer-learning model (TLM) demonstrated superior diagnostic performance compared to the basic deep-learning model, with higher area under the curves of 0.906 (95%CI: 0.872-0.940) vs 0.793 (0.743-0.843), 0.914 (0.875-0.953) vs 0.790 (0.727-0.853), and 0.907 (0.869-0.945) vs 0.880 (0.838-0.922) for the three independent test cohorts. Furthermore, when aided by the TLM, diagnostic accuracy surpassed that of individual sonologists alone. The TLM achieved satisfactory performance in predicting fetal BA, providing a low-cost, easily accessible, and accurate diagnostic tool for this condition, making it an effective aid in clinical practice.