ArticleOphthalmology. Retina2020
Variability in Plus Disease Identified Using a Deep Learning-Based Retinopathy of Prematurity Severity Scale.
Article in Ophthalmology. Retina, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- Opportunities and Challenges of Using Artificial Intelligence in Predicting Clinical Outcomes and Length of Stay in Neonatal Intensive Care Units: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence for the diagnosis of retinopathy of prematurity: A systematic review of current algorithms.Eye (London, England) · 2023Pooled 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
- Automatic Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Learning.Beyoglu eye journal · 2026Article
- Feasibility of Multimodal Deep Learning for Automated Staging of Familial Exudative Vitreoretinopathy Using Color Fundus Photographs and Fluorescein Angiography.Diagnostics (Basel, Switzerland) · 2025Article
- Foveal Identification and Development in Prematurity-Implications on Zone Localization and Nutritional Supplementation.Investigative ophthalmology & visual science · 2025Observational
- Image harmonization and deep learning automated classification of plus disease in retinopathy of prematurity.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Telemedicine retinopathy of prematurity severity score (TeleROP-SS) versus modified activity score (mROP-ActS) retrospective comparison in SUNDROP cohort.Scientific reports · 2023Article
- Development and validation of an artificial intelligence based screening tool for detection of retinopathy of prematurity in a South Indian population.Frontiers in pediatrics · 2023Article
- Evaluation of an Artificial Intelligence System for Retinopathy of Prematurity Screening in Nepal and Mongolia.Ophthalmology science · 2022Article
- Federated Learning for Multicenter Collaboration in Ophthalmology: Implications for Clinical Diagnosis and Disease Epidemiology.Ophthalmology. Retina · 2022Article
- Intravitreal bevacizumab to treat retinopathy of prematurity in 865 eyes: a study to determine predictors of primary treatment failure and recurrence.International ophthalmology · 2022Article
- Artificial Intelligence for Retinopathy of Prematurity: Validation of a Vascular Severity Scale against International Expert Diagnosis.Ophthalmology · 2022Article
- Article
- Applications of Deep Learning: Automated Assessment of Vascular Tortuosity in Mouse Models of Oxygen-Induced Retinopathy.Ophthalmology scienceArticle
- Performance of deep-learning artificial intelligence algorithms in detecting retinopathy of prematurity: A systematic review.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
purposeRetinopathy of prematurity is a leading cause of childhood blindness worldwide, but clinical diagnosis is subjective, which leads to treatment differences. Our goal was to determine objective differences in the diagnosis of plus disease between clinicians using an automated retinopathy of prematurity (ROP) vascular severity score.
designThis retrospective cohort study used data from the Imaging and Informatics in ROP Consortium, which comprises 8 tertiary care centers in North America. Fundus photographs of all infants undergoing ROP screening examinations between July 1, 2011, and December 31, 2016, were obtained.
participantsInfants meeting ROP screening criteria who were diagnosed with plus disease and treatment initiated by an examining physician based on ophthalmoscopic examination results.
methodsAn ROP severity score (1-9) was generated for each image using a deep learning (DL) algorithm.
main outcome measuresThe mean, median, and range of ROP vascular severity scores overall and for each examiner when the diagnosis of plus disease was made.
resultsA total of 5255 clinical examinations in 871 babies were analyzed. Of these, 168 eyes were diagnosed with plus disease by 11 different examiners and were included in the study. The mean ± standard deviation vascular severity score for patients diagnosed with plus disease was 7.4 ± 1.9, median was 8.5 (interquartile range, 5.8-8.9), and range was 1.1 to 9.0. Within some examiners, variability in the level of vascular severity diagnosed as plus disease was present, and 1 examiner routinely diagnosed plus disease in patients with less severe disease than the other examiners (P < 0.01).
conclusionsWe observed variability both between and within examiners in the diagnosis of plus disease using DL. Prospective evaluation of clinical trial data using an objective measurement of vascular severity may help to define better the minimum necessary level of vascular severity for the diagnosis of plus disease or how other clinical features such as zone, stage, and extent of peripheral disease ought to be incorporated in treatment decisions.
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