ArticleOphthalmology2021
Evaluation of a Deep Learning-Derived Quantitative Retinopathy of Prematurity Severity Scale.
Article in Ophthalmology, 2021. 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.
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Who cites it
42 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for the diagnosis of retinopathy of prematurity: A systematic review of current algorithms.Eye (London, England) · 2023Pooled it
- Pooled it
- Assessment of Retinopathy of Prematurity Regression and Reactivation Using an Artificial Intelligence-Based Vascular Severity Score.JAMA network open · 2023Trial
- Risk assessment and prediction of myopia among school-age children in South China: protocol for a longitudinal follow-up study.BMJ open · 2026Article
- 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
- Current Trends in AI and Eye Disease Diagnostics.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Review
- Quantifying Sagittal Craniosynostosis Severity: A Machine Learning Approach With CranioRate.The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association · 2026Article
- [Research progress in artificial intelligence for the diagnosis and management of diseases in preterm infants].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Review
- Automatic Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Learning.Beyoglu eye journal · 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
- 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
- Use of an Artificial Intelligence-Generated Vascular Severity Score Improved Plus Disease Diagnosis in Retinopathy of Prematurity.Ophthalmology · 2024Article
- Isolated Retinal Neovascularization in Retinopathy of Prematurity: Clinical Associations and Prognostic Implications.Ophthalmology. Retina · 2024Article
- A Review of the Utility and Limitations of Artificial Intelligence in Retinal Disorders and Pediatric Ophthalmology.Cureus · 2024Review
- An Interpretable System for Screening the Severity Level of Retinopathy in Premature Infants Using Deep Learning.Bioengineering (Basel, Switzerland) · 2024Article
- A fundus image dataset for intelligent retinopathy of prematurity system.Scientific data · 2024Article
- Automated diagnosis of plus disease in retinopathy of prematurity using quantification of vessels characteristics.Scientific reports · 2024Article
- Genome-wide association identifies novel ROP risk loci in a multiethnic cohort.Communications biology · 2024Article
- The performance of a deep learning system in assisting junior ophthalmologists in diagnosing 13 major fundus diseases: a prospective multi-center clinical trial.NPJ digital medicine · 2024Article
- Artificial intelligence in retinal disease: clinical application, challenges, and future directions.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023Review
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Funding
Abstract
purposeTo evaluate the clinical usefulness of a quantitative deep learning-derived vascular severity score for retinopathy of prematurity (ROP) by assessing its correlation with clinical ROP diagnosis and by measuring clinician agreement in applying a novel scale.
designAnalysis of existing database of posterior pole fundus images and corresponding ophthalmoscopic examinations using 2 methods of assigning a quantitative scale to vascular severity.
participantsImages were from clinical examinations of patients in the Imaging and Informatics in ROP Consortium. Four ophthalmologists and 1 study coordinator evaluated vascular severity on a scale from 1 to 9.
methodsA quantitative vascular severity score (1-9) was applied to each image using a deep learning algorithm. A database of 499 images was developed for assessment of interobserver agreement.
main outcome measuresDistribution of deep learning-derived vascular severity scores with the clinical assessment of zone (I, II, or III), stage (0, 1, 2, or 3), and extent (<3 clock hours, 3-6 clock hours, and >6 clock hours) of stage 3 evaluated using multivariate linear regression and weighted κ values and Pearson correlation coefficients for interobserver agreement on a 1-to-9 vascular severity scale.
resultsFor deep learning analysis, a total of 6344 clinical examinations were analyzed. A higher deep learning-derived vascular severity score was associated with more posterior disease, higher disease stage, and higher extent of stage 3 disease (P < 0.001 for all). For a given ROP stage, the vascular severity score was higher in zone I than zones II or III (P < 0.001). Multivariate regression found zone, stage, and extent all were associated independently with the severity score (P < 0.001 for all). For interobserver agreement, the mean ± standard deviation weighted κ value was 0.67 ± 0.06, and the Pearson correlation coefficient ± standard deviation was 0.88 ± 0.04 on the use of a 1-to-9 vascular severity scale.
conclusionsA vascular severity scale for ROP seems feasible for clinical adoption; corresponds with zone, stage, extent of stage 3, and plus disease; and facilitates the use of objective technology such as deep learning to improve the consistency of ROP diagnosis.
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