ArticleThe British journal of ophthalmology2018
Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity.
Article in The British journal of ophthalmology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 1 of them a synthesis that pooled it.
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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.
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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
81 citing papers in PubMed, 1 synthesis or guideline pooled it, 177 citations in OpenAlex.
- 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
- Review
- Quantitative Assessment of the Extent of Retinal Vascularization in Retinopathy of Prematurity.Ophthalmology science · 2026Article
- Leveraging deep learning to infer continuous predictions from ordinal labels in medical imaging.PLOS digital health · 2026Article
- Artificial Intelligence in the Detection and Risk Stratification of Choroidal Melanoma: A Critical Comparative Synthesis and Future Directions.Healthcare (Basel, Switzerland) · 2025Review
- Review of Retinopathy of Prematurity Management in the Anti-VEGF Era: Evolving Global Paradigms, Persistent Challenges and Our AI-Assisted Future.Clinical & experimental ophthalmology · 2025Review
- 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
- Differentiation of optic disc edema and pseudopapilledema with deep learning on near-infrared reflectance images.BMC ophthalmology · 2025Article
- The application of artificial intelligence-generated content in ophthalmology education.Frontiers in medicine · 2025Review
- Implementation of Artificial Intelligence in Retinopathy of Prematurity Care: Challenges and Opportunities.International ophthalmology clinics · 2024Review
- Isolated Retinal Neovascularization in Retinopathy of Prematurity: Clinical Associations and Prognostic Implications.Ophthalmology. Retina · 2024Article
- Automated detection of type 1 ROP, type 2 ROP and A-ROP based on deep learning.Eye (London, England) · 2024Article
- An Interpretable System for Screening the Severity Level of Retinopathy in Premature Infants Using Deep Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Multi-risk factors joint prediction model for risk prediction of retinopathy of prematurity.The EPMA journal · 2024Article
- Congenital diaphragmatic hernia: automatic lung and liver MRI segmentation with nnU-Net, reproducibility of pyradiomics features, and a machine learning application for the classification of liver herniation.European journal of pediatrics · 2024Article
- Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases.Medicina (Kaunas, Lithuania) · 2024Review
- Automated diagnosis of plus disease in retinopathy of prematurity using quantification of vessels characteristics.Scientific reports · 2024Article
- The incidence of retinopathy of prematurity in neonates in Germany in 2019; a nationwide epidemiological cohort study.European journal of pediatrics · 2024Article
- Development and validation of a semi-supervised deep learning model for automatic retinopathy of prematurity staging.iScience · 2024Article
21 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors at 6 institutions in 2 countries.
Funding
Abstract
backgroundPrior work has demonstrated the near-perfect accuracy of a deep learning retinal image analysis system for diagnosing plus disease in retinopathy of prematurity (ROP). Here we assess the screening potential of this scoring system by determining its ability to detect all components of ROP diagnosis.
methodsClinical examination and fundus photography were performed at seven participating centres. A deep learning system was trained to detect plus disease, generating a quantitative assessment of retinal vascular abnormality (the i-ROP plus score) on a 1-9 scale. Overall ROP disease category was established using a consensus reference standard diagnosis combining clinical and image-based diagnosis. Experts then ranked ordered a second data set of 100 posterior images according to overall ROP severity.
results4861 examinations from 870 infants were analysed. 155 examinations (3%) had a reference standard diagnosis of type 1 ROP. The i-ROP deep learning (DL) vascular severity score had an area under the receiver operating curve of 0.960 for detecting type 1 ROP. Establishing a threshold i-ROP DL score of 3 conferred 94% sensitivity, 79% specificity, 13% positive predictive value and 99.7% negative predictive value for type 1 ROP. There was strong correlation between expert rank ordering of overall ROP severity and the i-ROP DL vascular severity score (Spearman correlation coefficient=0.93; p<0.0001).
conclusionThe i-ROP DL system accurately identifies diagnostic categories and overall disease severity in an automated fashion, after being trained only on posterior pole vascular morphology. These data provide proof of concept that a deep learning screening platform could improve objectivity of ROP diagnosis and accessibility of screening.
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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.