Observational studyOphthalmology2016
Diagnostic Discrepancies in Retinopathy of Prematurity Classification.
Observational study in Ophthalmology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 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
42 citing papers in PubMed, 2 syntheses or guidelines pooled it, 76 citations in OpenAlex.
- Retinopathy of prematurity training and education: A systematic review.Survey of ophthalmologyPooled it
- Addressing the Third Epidemic of Retinopathy of Prematurity Through Telemedicine and Technology: A Systematic Review.Journal of pediatric ophthalmology and strabismusPooled it
- 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
- Quantitative Assessment of the Extent of Retinal Vascularization in Retinopathy of Prematurity.Ophthalmology science · 2026Article
- Automated Feature Segmentation of Ultra-Widefield OCT Images.Ophthalmology science · 2026Article
- External validation of an artificial intelligence-based model for retinopathy of prematurity screening using Phoenix ICON retinal images.Journal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus · 2025Article
- Does field of view matter? Assessing its impact on Plus disease diagnosis in retinopathy of prematurity.International journal of retina and vitreous · 2025Article
- Establishing an objective quantitative method for assessing the severity of plus disease in retinopathy of prematurity.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Article
- Computer-aided detection of retinopathy of prematurity severity assessment via vessel tortuosity measurement in preterm infants' fundus images.Eye (London, England) · 2024Article
- Evaluating the Feasibility of a Telescreening Program for Retinopathy of Prematurity (ROP) in Denmark.Journal of personalized medicine · 2024Article
- Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence.Journal of personalized medicine · 2024Review
- Ocular blood flow in preterm neonates.Scientific reports · 2024Article
- Mechanical Ventilation, Retinal Avascularity and Rate of Vascularisation: A Triad of Predictors for Retinopathy of Prematurity Treatment.Journal of personalized medicine · 2024Article
- Validation of DIGIROP models and decision support tool for prediction of treatment for retinopathy of prematurity on a contemporary Swedish cohort.The British journal of ophthalmology · 2023Article
- Development and international validation of custom-engineered and code-free deep-learning models for detection of plus disease in retinopathy of prematurity: a retrospective study.The Lancet. Digital health · 2023Article
- Evaluation of an Artificial Intelligence System for Retinopathy of Prematurity Screening in Nepal and Mongolia.Ophthalmology science · 2022Article
- Variability in Plus Disease Diagnosis using Single and Serial Images.Ophthalmology. Retina · 2022Article
- ADS-Net: attention-awareness and deep supervision based network for automatic detection of retinopathy of prematurity.Biomedical optics express · 2022Article
- Federated Learning for Multicenter Collaboration in Ophthalmology: Implications for Clinical Diagnosis and Disease Epidemiology.Ophthalmology. Retina · 2022Article
- Artificial Intelligence for Retinopathy of Prematurity: Validation of a Vascular Severity Scale against International Expert Diagnosis.Ophthalmology · 2022Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
purposeTo identify the most common areas for discrepancy in retinopathy of prematurity (ROP) classification between experts.
designProspective cohort study.
participantsA total of 281 infants were identified as part of a multicenter, prospective, ROP cohort study from 7 participating centers. Each site had participating ophthalmologists who provided the clinical classification after routine examination using binocular indirect ophthalmoscopy (BIO) and obtained wide-angle retinal images, which were independently classified by 2 study experts.
methodsWide-angle retinal images (RetCam; Clarity Medical Systems, Pleasanton, CA) were obtained from study subjects, and 2 experts evaluated each image using a secure web-based module. Image-based classifications for zone, stage, plus disease, and overall disease category (no ROP, mild ROP, type II or pre-plus, and type I) were compared between the 2 experts and with the clinical classification obtained by BIO.
main outcome measuresInter-expert image-based agreement and image-based versus ophthalmoscopic diagnostic agreement using absolute agreement and weighted kappa statistic.
resultsA total of 1553 study eye examinations from 281 infants were included in the study. Experts disagreed on the stage classification in 620 of 1553 comparisons (40%), plus disease classification (including pre-plus) in 287 of 1553 comparisons (18%), zone in 117 of 1553 comparisons (8%), and overall ROP category in 618 of 1553 comparisons (40%). However, agreement for presence versus absence of type 1 disease was >95%. There were no differences between image-based and clinical classification except for zone III disease.
conclusionsThe most common area of discrepancy in ROP classification is stage, although inter-expert agreement for clinically significant disease, such as presence versus absence of type 1 and type 2 disease, is high. There were no differences between image-based grading and clinical examination in the ability to detect clinically significant disease. This study provides additional evidence that image-based classification of ROP reliably detects clinically significant levels of ROP with high accuracy compared with the clinical examination.
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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.