ArticleTranslational vision science & technology2023
Multivariate Models to Diagnose Early Referral-Warranted Retinopathy of Prematurity With Handheld Optical Coherence Tomography.
Article in Translational vision science & technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence-based algorithms for the diagnosis of retinopathy of prematurity.The Cochrane database of systematic reviews · 2025Pooled it
- In-depth analysis of research hotspots and emerging trends in AI for retinal diseases over the past decade.Frontiers in medicine · 2024Pooled it
- Preterm Infant Stress During Noncontact Handheld Optical Coherence Tomography and Contact Fundus Photography.JAMA ophthalmology · 2026Article
- Validating DevCheck: a semi-automated computer algorithm for measuring foveal immaturity in premature infants.Quantitative imaging in medicine and surgery · 2026Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Purpose: The purpose of this study was to create multivariate models predicting early referral-warranted retinopathy of prematurity (ROP) using non-contact handheld spectral-domain optical coherence tomography (OCT) and demographic data. Methods: Between July 2015 and February 2018, infants ≤1500 grams birth weight or ≤30 weeks gestational age from 2 academic neonatal intensive care units were eligible for this study. Infants were excluded if they were too unstable to participate in ophthalmologic examination (2), had inadequate image quality (20), or received prior ROP treatment (2). Multivariate models were created using demographic variables and imaging findings to identify early referral-warranted ROP (referral-warranted ROP and/or pre-plus disease) by routine indirect ophthalmoscopy. Results: A total of 167 imaging sessions of 71 infants (45% male infants, gestational age 28.2+/-2.8 weeks, and birth weight 995.6+/-292.0 grams) were included. Twelve of 71 infants (17%) developed early referral-warranted ROP. The area under the receiver operating characteristic curve (AUC) was 0.94 for the generalized linear mixed model (sensitivity = 95.5% and specificity = 80.7%) and 0.83 for the machine learning model (sensitivity = 91.7% and specificity = 77.8%). The strongest variables in both models were birth weight, image-based Vitreous Opacity Ratio (an estimate of opacity density), vessel elevation, and hyporeflective vessels. A model using only birth weight and gestational age yielded an AUC of 0.68 (sensitivity = 77.3% and specificity = 63.4%), and a model using only imaging biomarkers yielded 0.88 (sensitivity = 81.8% and specificity = 84.8%). Conclusions: A generalized linear mixed model containing handheld OCT biomarkers can identify early referral-warranted ROP. Machine learning produced a less optimal model. Translational Relevance: With further validation, this work may lead to a better-tolerated ROP screening tool.
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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.