Applying AI models to digital placental photographs to automate and improve morphology assessments
AI-based measurements were less than a cm from human measurements overall. Our findings of larger differences for irregular shapes and preterm may indicate it is difficult for humans to measure irregular or small placentas according to protocol. PlacentaVision can automate and standardize the process.
Authors
- Rachel E. Walker (ORCID: https://orcid.org/0000-0002-8845-858X)
- Gwen Sincerbeaux
- Lisa M. Bebell (ORCID: https://orcid.org/0000-0003-0077-6522)
- Alison D. Gernand (ORCID: https://orcid.org/0000-0002-5661-7785)
- M. Mehta
- Kelly Gallagher (ORCID: https://orcid.org/0000-0002-3291-3512)
- James Z. Wang
- Janet Catov
- Jeffery GOLDSTEIN
- Lauren B Skvarca
- Joseph Ngonzi
- Yimu Pan
Institutions
- Pennsylvania State University (US)
- Mbarara University of Science and Technology (UG)
- Northwestern Memorial Hospital (US)
- Magee-Womens Research Institute (US)
- Center for Global Health (US)
- University of Pittsburgh Medical Center (US)
- Harvard Global Health Institute (US)
Publication Details
- Journal
- medRxiv
- Published
- 2026-03-02
- DOI
- https://doi.org/10.64898/2026.02.28.26347346
- Primary Topic
- Pregnancy and preeclampsia studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- Andrew W. Mellon Foundation
- American Society of Tropical Medicine and Hygiene
- Northwestern University
- University of Pittsburgh
- Magee-Womens Research Institute
- National Institutes of Health
- National Institute of Child Health and Human Development
- Office of Advanced Cyberinfrastructure