ArticleTranslational vision science & technology2015
Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the "i-ROP" System and Image Features Associated With Expert Diagnosis.
Article in Translational vision science & technology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 1 of them a synthesis that pooled it.
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Who cites it
66 citing papers in PubMed, 1 synthesis or guideline pooled it, 141 citations in OpenAlex.
- Addressing the Third Epidemic of Retinopathy of Prematurity Through Telemedicine and Technology: A Systematic Review.Journal of pediatric ophthalmology and strabismusPooled it
- Assessment of Retinopathy of Prematurity Regression and Reactivation Using an Artificial Intelligence-Based Vascular Severity Score.JAMA network open · 2023Trial
- Assessment of a Tele-education System to Enhance Retinopathy of Prematurity Training by International Ophthalmologists-in-Training in Mexico.Ophthalmology · 2017Trial
- 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
- Artificial intelligence in pediatrics: a bibliometric analysis of global output, networks, and frontiers [2016-2025].Translational pediatrics · 2026Article
- 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
- AI models in clinical neonatology: a review of modeling approaches and a consensus proposal for standardized reporting of model performance.Pediatric research · 2025Review
- 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
- Machine learning demonstrates clinical utility in distinguishing retinoblastoma from pseudo retinoblastoma with RetCam images.Ophthalmic genetics · 2025Article
- Association between retinal vascular fractal dimensions and retinopathy of prematurity: an AI-assisted retrospective case-control study.International ophthalmology · 2025Article
- Computer-aided detection of retinopathy of prematurity severity assessment via vessel tortuosity measurement in preterm infants' fundus images.Eye (London, England) · 2024Article
- A Review of the Utility and Limitations of Artificial Intelligence in Retinal Disorders and Pediatric Ophthalmology.Cureus · 2024Review
- Automated detection of type 1 ROP, type 2 ROP and A-ROP based on deep learning.Eye (London, England) · 2024Article
- Retinal Image Dataset of Infants and Retinopathy of Prematurity.Scientific data · 2024Article
- Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence.Journal of personalized medicine · 2024Review
- Automated diagnosis of plus disease in retinopathy of prematurity using quantification of vessels characteristics.Scientific reports · 2024Article
- Changes of the peripapillary vascular parameters in premature infants without retinopathy of prematurity using U-net segmentation.International journal of ophthalmology · 2024Article
- The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review.NPJ digital medicine · 2023Review
- Vascular features around the optic disc in familial exudative vitreoretinopathy: findings and their relationship to disease severity.BMC ophthalmology · 2023Article
- GabROP: Gabor Wavelets-Based CAD for Retinopathy of Prematurity Diagnosis via Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 2 countries.
Funding
Abstract
purposeWe developed and evaluated the performance of a novel computer-based image analysis system for grading plus disease in retinopathy of prematurity (ROP), and identified the image features, shapes, and sizes that best correlate with expert diagnosis.
methodsA dataset of 77 wide-angle retinal images from infants screened for ROP was collected. A reference standard diagnosis was determined for each image by combining image grading from 3 experts with the clinical diagnosis from ophthalmoscopic examination. Manually segmented images were cropped into a range of shapes and sizes, and a computer algorithm was developed to extract tortuosity and dilation features from arteries and veins. Each feature was fed into our system to identify the set of characteristics that yielded the highest-performing system compared to the reference standard, which we refer to as the "i-ROP" system.
resultsAmong the tested crop shapes, sizes, and measured features, point-based measurements of arterial and venous tortuosity (combined), and a large circular cropped image (with radius 6 times the disc diameter), provided the highest diagnostic accuracy. The i-ROP system achieved 95% accuracy for classifying preplus and plus disease compared to the reference standard. This was comparable to the performance of the 3 individual experts (96%, 94%, 92%), and significantly higher than the mean performance of 31 nonexperts (81%).
conclusionsThis comprehensive analysis of computer-based plus disease suggests that it may be feasible to develop a fully-automated system based on wide-angle retinal images that performs comparably to expert graders at three-level plus disease discrimination. TRANSLATIONAL RELEVANCE: Computer-based image analysis, using objective and quantitative retinal vascular features, has potential to complement clinical ROP diagnosis by ophthalmologists.
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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.