ArticleJAMA ophthalmology2016
Expert Diagnosis of Plus Disease in Retinopathy of Prematurity From Computer-Based Image Analysis.
Article in JAMA ophthalmology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.
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Who cites it
69 citing papers in PubMed.
- Assessment of Retinopathy of Prematurity Regression and Reactivation Using an Artificial Intelligence-Based Vascular Severity Score.JAMA network open · 2023Trial
- An international comparison of retinopathy of prematurity grading performance within the Benefits of Oxygen Saturation Targeting II trials.Eye (London, England) · 2018Trial
- Assessment of a Tele-education System to Enhance Retinopathy of Prematurity Training by International Ophthalmologists-in-Training in Mexico.Ophthalmology · 2017Trial
- Comparison of Two AI Chatbots for Diagnosis and Providing Treatment Suggestions in Retinopathy of Prematurity: Retrospective Study.Journal of medical Internet research · 2026Article
- Automatic Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Learning.Beyoglu eye journal · 2026Article
- OCT-Derived Quantitative Measurement of Extent of Vascularization ("Zone") in Retinopathy of Prematurity.Ophthalmology science · 2026Article
- Explainability-informed patient-disjoint evaluation of deep learning for plus disease in retinopathy of prematurity with an ophthalmologist reader study.Frontiers in medicine · 2026Article
- Foveal Identification and Development in Prematurity-Implications on Zone Localization and Nutritional Supplementation.Investigative ophthalmology & visual science · 2025Observational
- Development and validation of a deep learning image quality feedback system for infant fundus photography.Scientific reports · 2025Article
- SmartPlus: a computer-based image analysis method to predict continuous-valued vascular abnormality index 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
- Review
- Bibliometric analysis of research on the application of deep learning to ophthalmology.Quantitative imaging in medicine and surgery · 2025Article
- Beyond anti-VEGF: expanding the therapeutic horizon and biomarker landscape in retinopathy of prematurity.Frontiers in pediatrics · 2025Review
- Computer-aided detection of retinopathy of prematurity severity assessment via vessel tortuosity measurement in preterm infants' fundus images.Eye (London, England) · 2024Article
- Automated detection of type 1 ROP, type 2 ROP and A-ROP based on deep learning.Eye (London, England) · 2024Article
- Role of Artificial Intelligence in Retinal Diseases.Klinische Monatsblatter fur Augenheilkunde · 2024Review
- Automated diagnosis of plus disease in retinopathy of prematurity using quantification of vessels characteristics.Scientific reports · 2024Article
- Applying deep learning to recognize the properties of vitreous opacity in ophthalmic ultrasound images.Eye (London, England) · 2024Article
- A clinical informatics approach to bronchopulmonary dysplasia: current barriers and future possibilities.Frontiers in pediatrics · 2024Review
9 more citing papers are in PubMed but not listed here.
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Authors and funding
20 authors.
Funding
Abstract
importancePublished definitions of plus disease in retinopathy of prematurity (ROP) reference arterial tortuosity and venous dilation within the posterior pole based on a standard published photograph. One possible explanation for limited interexpert reliability for a diagnosis of plus disease is that experts deviate from the published definitions.
objectiveTo identify vascular features used by experts for diagnosis of plus disease through quantitative image analysis. DESIGN, SETTING, AND
participantsA computer-based image analysis system (Imaging and Informatics in ROP [i-ROP]) was developed using a set of 77 digital fundus images, and the system was designed to classify images compared with a reference standard diagnosis (RSD). System performance was analyzed as a function of the field of view (circular crops with a radius of 1-6 disc diameters) and vessel subtype (arteries only, veins only, or all vessels). Routine ROP screening was conducted from June 29, 2011, to October 14, 2014, in neonatal intensive care units at 8 academic institutions, with a subset of 73 images independently classified by 11 ROP experts for validation. The RSD was compared with the majority diagnosis of experts. MAIN OUTCOMES AND MEASURES: The primary outcome measure was the percentage of accuracy of the i-ROP system classification of plus disease, with the RSD as a function of the field of view and vessel type. Secondary outcome measures included the accuracy of the 11 experts compared with the RSD.
resultsAccuracy of plus disease diagnosis by the i-ROP computer-based system was highest (95%; 95% CI, 94%-95%) when it incorporated vascular tortuosity from both arteries and veins and with the widest field of view (6-disc diameter radius). Accuracy was 90% or less when using only arterial tortuosity and 85% or less using a 2- to 3-disc diameter view similar to the standard published photograph. Diagnostic accuracy of the i-ROP system (95%) was comparable to that of 11 expert physicians (mean 87%, range 79%-99%). CONCLUSIONS AND RELEVANCE: Experts in ROP appear to consider findings from beyond the posterior retina when diagnosing plus disease and consider tortuosity of both arteries and veins, in contrast with published definitions. It is feasible for a computer-based image analysis system to perform comparably with ROP experts, using manually segmented images.
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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.