Transfer learning method for prenatal ultrasound diagnosis of biliary atresia

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.

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Publication Details

Journal
npj Digital Medicine
Published
2025-02-28
DOI
https://doi.org/10.1038/s41746-025-01525-1
Citations
2
Primary Topic
Pediatric Hepatobiliary Diseases and Treatments
Type
article
Field-Weighted Citation Impact
4.01
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article

Transfer learning method for prenatal ultrasound diagnosis of biliary atresia

Ming‐Ming Shao, Lizhu Chen, Fujiao He, Kaihui Zeng et al.
2 citations
npj Digital Medicine
Pediatric Hepatobiliary Diseases and Treatments
4.01
article

Transfer learning method for prenatal ultrasound diagnosis of biliary atresia

Ming‐Ming Shao, Lizhu Chen, Fujiao He, Kaihui Zeng, Luyao Zhou, Jiaxi Yao, Zhengwei Yuan, X.C. Li, Zeyu Yang, Zhibo Zhang, Gang Li, Yu Wang, Dan Zhao, Mo Zhang, Hao Ding, Zhichao Zhang, Chaoran Yang, Zongjie Weng, B. Hong, Jiao Yin, Wei Sun
article en
2 citations

Abstract

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.

npj Digital MedicineVol. 8(1)
Fujian Medical University (CN), First People's Hospital of Yunnan Province (CN), Chinese University of Hong Kong, Shenzhen (CN), First Hospital of China Medical University (CN), Shenzhen Children's Hospital (CN), China Medical University (CN), Northeastern University (CN)
Openalex Percentile: Top 9%
Pediatric Hepatobiliary Diseases and Treatments
4.01
DOIOpen Access PDFOpenAlex
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