AI-Enabled Screening for Retinopathy of Prematurity in Low-Resource Settings
In this diagnostic study, a process that used smartphone-collected videos of premature neonates' fundi to determine whether high-quality retinal images were present had high sensitivity to classify such images as indicating or not indicating ROP but lower specificity and accuracy than ophthalmologist assessment. This process costs a fraction of the current process for retinal image collection and classification and could be used to expand access to ROP screening in low-resource settings, with potential to help prevent the most common cause of preventable childhood blindness.
Authors
- María Ana Martinez-Castellanos (ORCID: https://orcid.org/0000-0002-1265-3725)
- Brenda Peña
- Vanina Schbib (ORCID: https://orcid.org/0000-0001-6713-4618)
- Josep María Dalmau Torres (ORCID: https://orcid.org/0000-0002-8003-9241)
- Gabriela Saidman (ORCID: https://orcid.org/0000-0002-8255-2057)
- Rahul Dodhia (ORCID: https://orcid.org/0000-0003-3812-3906)
- Juan Lavista Ferres (ORCID: https://orcid.org/0000-0002-9654-3178)
- William B. Weeks (ORCID: https://orcid.org/0000-0002-9918-1360)
- Guillermo Monteoliva (ORCID: https://orcid.org/0000-0001-7547-265X)
- Colleen Serafin
- Juan Mármol
- Susana Patiño
- Anthony Ortiz (ORCID: https://orcid.org/0009-0001-5722-5273)
Institutions
- Microsoft (United States) (US)
- Hospital Italiano de Buenos Aires (AR)
- MSD (Mexico) (MX)
- Asociacion Para Evitar la Ceguera en México Hospital Dr. Luis Sánchez Bulnes (MX)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2025-04-29
- DOI
- https://doi.org/10.1001/jamanetworkopen.2025.7831
- Citations
- 8
- Primary Topic
- Retinopathy of Prematurity Studies
- Type
- article
- Field-Weighted Citation Impact
- 10.88