Machine Learning Models for Predicting Treatment-Requiring Retinopathy of Prematurity in the e-ROP Study
ML has the potential to predict TR-ROP risk based on early image findings, demographics, and clinical characteristics.
Authors
- Agnieshka Baumritter (ORCID: https://orcid.org/0000-0003-3647-4921)
- Xiang Luo (ORCID: https://orcid.org/0000-0002-9438-5225)
- Bowen Ying
- Lifang He (ORCID: https://orcid.org/0000-0001-7810-9071)
- Graham E. Quinn (ORCID: https://orcid.org/0000-0002-9006-3464)
- Gui‐Shuang Ying (ORCID: https://orcid.org/0000-0002-5752-1355)
- Dongjie He (ORCID: https://orcid.org/0000-0003-0304-8942)
- Yong Chen (ORCID: https://orcid.org/0000-0003-0835-0788)
Institutions
- Children's Hospital of Philadelphia (US)
- Penn Presbyterian Medical Center (US)
- Lehigh University (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- Translational Vision Science & Technology
- Published
- 2025-08-11
- DOI
- https://doi.org/10.1167/tvst.14.8.14
- Citations
- 1
- Primary Topic
- Retinopathy of Prematurity Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- U.S. Department of Health and Human Services
- National Institutes of Health