Development of an artificial intelligence-based multimodal diagnostic system for early detection of biliary atresia
This AI-based screening application effectively facilitates early diagnosis of BA and serves as a valuable reference for addressing common challenges in rare diseases. The model's high accuracy and its ability to enhance the diagnostic performance of human experts underscore its potential for significant clinical impact.
Authors
- L. C. Liu
- Yuxin Du (ORCID: https://orcid.org/0000-0001-7429-3238)
- Zelong Jin (ORCID: https://orcid.org/0000-0001-8370-1758)
- Chao Tong (ORCID: https://orcid.org/0000-0003-4414-4965)
- Ya Ma (ORCID: https://orcid.org/0000-0002-6276-4060)
- Yuancheng Yang (ORCID: https://orcid.org/0000-0002-1453-8077)
- Zhimin Qiu
- Baoyu Liang (ORCID: https://orcid.org/0000-0002-2365-589X)
- Yiheng Wang (ORCID: https://orcid.org/0009-0001-9668-1722)
- Mao Ye (ORCID: https://orcid.org/0000-0002-5178-6950)
- Zijian Zhang (ORCID: https://orcid.org/0000-0002-1748-4924)
- Zhengrong Wang (ORCID: https://orcid.org/0000-0001-7471-5904)
- Yuqi Zhang (ORCID: https://orcid.org/0000-0002-8198-2282)
- Luyang Jin
Institutions
- Capital Institute of Pediatrics (CN)
- Beihang University (CN)
Publication Details
- Journal
- BMC Medicine
- Published
- 2025-02-27
- DOI
- https://doi.org/10.1186/s12916-025-03962-x
- Citations
- 8
- Primary Topic
- Pediatric Hepatobiliary Diseases and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 16.05
Funders
- National Natural Science Foundation of China