Construction and validation of a machine learning model integrating ultrasound features and inflammatory markers (OVART-ML) for predicting ovarian torsion and ischemic necrosis risk in children
This study developed an interpretable ML model that could accurately predict the risks of OT and IN in children. Key factors such as the follicular edema ring sign and vomiting provide important references for early diagnosis and intervention. This tool may assist clinicians in making timely surgical decisions to preserve ovarian function in children.
Authors
- Binyi Yang
- Yuezhen Zhang
- Yubing Wang (ORCID: https://orcid.org/0009-0001-4369-4693)
- Jinbin Wang (ORCID: https://orcid.org/0000-0001-7080-4264)
- Shujie Song (ORCID: https://orcid.org/0009-0009-3350-9747)
- Jiaxiang Tang (ORCID: https://orcid.org/0000-0003-2487-4469)
- Zhifei Zhao
- Hongting Lu (ORCID: https://orcid.org/0000-0003-4981-1675)
Institutions
- Qingdao University (CN)
- Linyi People's Hospital (CN)
- Qingdao Women and Children's Hospital (CN)
Publication Details
- Journal
- Frontiers in Pediatrics
- Published
- 2025-12-04
- DOI
- https://doi.org/10.3389/fped.2025.1717545
- Primary Topic
- Ovarian cancer diagnosis and treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00