Health AI Central
Peer-Reviewed Publication
Neonatology2026;123(2):200-208.January 1, 2026Journal Article

Machine Learning Risk Prediction for Treated Retinopathy of Prematurity in Infants.

Henry P Foote1, Yanchen J Ou2, Suchir Bhatt3, Matthew M Engelhard3, Leonid Bederman4, Matthew M Laughon5, Kanecia O Zimmerman1,6, Rishikesan Kamaleswaran7,8, Rachel G Greenberg1,6, Veeral N Tolia9, Christoph P Hornik1,6, Ricardo Henao3,8, Karan R Kumar10,11
1Department of Pediatrics, Duke University, Durham, North Carolina, USA.
2Department of Computer Science, Duke University, Durham, North Carolina, USA.
3Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.
4Department of Pediatrics, Emory University, Atlanta, Georgia, USA.
5Department of Pediatrics, School of Medicine, University of North Carolina Chapel Hill, Chapel Hill, North Carolina, USA.
6Duke Clinical Research Institute, Durham, North Carolina, USA.
7Department of Surgery, Duke University Medical Center, Durham, North Carolina, USA.
8Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA.
9Pediatrix Center for Research, Quality, Education and Safety, Sunrise, Florida, USA.
10Department of Pediatrics, Duke University, Durham, North Carolina, USA, karan.kumar@duke.edu.
11Duke Clinical Research Institute, Durham, North Carolina, USA, karan.kumar@duke.edu.
PubMed PMC DOI PDF

Abstract

INTRODUCTION: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. However, current screening guidelines may be overly broad, necessitating better models to detect high-risk infants. METHODS: From a multicenter cohort of 103,701 infants (3,301 [3.2%] treated for ROP) discharged from 298 neonatal intensive care units from 2006 to 2017 with birth weight ≤1,500 grams or gestat…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free AccountSign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.