ArticlePediatrics2021
Applications of Artificial Intelligence for Retinopathy of Prematurity Screening.
Article in Pediatrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
46 citing papers in PubMed, 3 syntheses or guidelines pooled it, 92 citations in OpenAlex.
- Artificial intelligence-based algorithms for the diagnosis of retinopathy of prematurity.The Cochrane database of systematic reviews · 2025Pooled it
- Artificial Intelligence (AI) and Healthcare Capabilities: A Systematic Review and Research Directions.F1000Research · 2025Pooled it
- Artificial intelligence for the diagnosis of retinopathy of prematurity: A systematic review of current algorithms.Eye (London, England) · 2023Pooled it
- Assessment of Retinopathy of Prematurity Regression and Reactivation Using an Artificial Intelligence-Based Vascular Severity Score.JAMA network open · 2023Trial
- Application of a Quantitative Vascular Severity Score in Retinopathy of Prematurity in the United States and India: New Insights Into Disease Epidemiology and Pathophysiology.American journal of ophthalmology · 2026Article
- Prediction of retinopathy of prematurity using machine learning models.Pediatric research · 2026Article
- Customizing AI-based screening with real-world data: Practical insights from diabetic retinopathy.Acta ophthalmologica · 2026Observational
- Artificial intelligence in pediatrics: a bibliometric analysis of global output, networks, and frontiers [2016-2025].Translational pediatrics · 2026Article
- Prediction of Retinopathy of Prematurity and Treatment in Very Low Birth Weight Infants Using Machine Learning on Nationwide Non-Imaging Clinical Data.Neonatology · 2026Article
- Optimizing retinopathy of prematurity screening in China using a single objective criterion: a 10-year retrospective analysis.Frontiers in pediatrics · 2026Article
- Artificial Intelligence in the Detection and Risk Stratification of Choroidal Melanoma: A Critical Comparative Synthesis and Future Directions.Healthcare (Basel, Switzerland) · 2025Review
- Construction of a predictive model for retinopathy of prematurity using machine learning algorithms.BMC ophthalmology · 2025Article
- Do neonatal intensive care unit (NICU) health workers know about retinopathy of prematurity (ROP)? A qualitative study at a Regional Referral Hospital in Uganda.BMJ paediatrics open · 2025Article
- Clinical effects of the 2021 update of the Turkish retinopathy of prematurity guideline.BMC ophthalmology · 2025Article
- Development and validation of machine learning classifiers for predicting treatment-needed retinopathy of prematurity.BMC medical informatics and decision making · 2025Article
- Diagnostic Accuracy of Pediatrician-performed Digital Retinal Imaging with 3nethra neo for ROP Screening.Indian journal of pediatrics · 2025Article
- Use of an Artificial Intelligence-Generated Vascular Severity Score Improved Plus Disease Diagnosis in Retinopathy of Prematurity.Ophthalmology · 2024Article
- Implementation of Artificial Intelligence in Retinopathy of Prematurity Care: Challenges and Opportunities.International ophthalmology clinics · 2024Review
- Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review.Ophthalmology and therapy · 2024Review
- Reassessing acquired neonatal intestinal diseases using unsupervised machine learning.Pediatric research · 2024Article
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Authors and funding
14 authors at 5 institutions in 3 countries.
Funding
Abstract
objectivesChildhood blindness from retinopathy of prematurity (ROP) is increasing as a result of improvements in neonatal care worldwide. We evaluate the effectiveness of artificial intelligence (AI)-based screening in an Indian ROP telemedicine program and whether differences in ROP severity between neonatal care units (NCUs) identified by using AI are related to differences in oxygen-titrating capability.
methodsExternal validation study of an existing AI-based quantitative severity scale for ROP on a data set of images from the Retinopathy of Prematurity Eradication Save Our Sight ROP telemedicine program in India. All images were assigned an ROP severity score (1-9) by using the Imaging and Informatics in Retinopathy of Prematurity Deep Learning system. We calculated the area under the receiver operating characteristic curve and sensitivity and specificity for treatment-requiring retinopathy of prematurity. Using multivariable linear regression, we evaluated the mean and median ROP severity in each NCU as a function of mean birth weight, gestational age, and the presence of oxygen blenders and pulse oxygenation monitors.
resultsThe area under the receiver operating characteristic curve for detection of treatment-requiring retinopathy of prematurity was 0.98, with 100% sensitivity and 78% specificity. We found higher median (interquartile range) ROP severity in NCUs without oxygen blenders and pulse oxygenation monitors, most apparent in bigger infants (>1500 g and 31 weeks' gestation: 2.7 [2.5-3.0] vs 3.1 [2.4-3.8];
conclusionsIntegration of AI into ROP screening programs may lead to improved access to care for secondary prevention of ROP and may facilitate assessment of disease epidemiology and NCU resources.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.