ArticleTranslational vision science & technology2019
Deep Learning Algorithm for Automated Diagnosis of Retinopathy of Prematurity Plus Disease.
Article in Translational vision science & technology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.
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Who cites it
37 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
- Performance and comparison of artificial intelligence and human experts in the detection and classification of colonic polyps.BMC gastroenterology · 2022Pooled it
- Automatic Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Learning.Beyoglu eye journal · 2026Article
- Research on the construction of an AI diagnostic model for plus disease of retinopathy of prematurity based on cross-center fusion datasets.Frontiers in pediatrics · 2026Article
- A novel method of quantifying retinal vascular tortuosity in retinopathy of prematurity.Scientific reports · 2025Article
- Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study.Journal of medical Internet research · 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
- Deep learning applications for diabetic retinopathy and retinopathy of prematurity diseases diagnosis: a systematic review.International journal of ophthalmology · 2025Review
- Computer-aided detection of retinopathy of prematurity severity assessment via vessel tortuosity measurement in preterm infants' fundus images.Eye (London, England) · 2024Article
- An Interpretable System for Screening the Severity Level of Retinopathy in Premature Infants Using Deep Learning.Bioengineering (Basel, Switzerland) · 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
- Automated diagnosis of plus disease in retinopathy of prematurity using quantification of vessels characteristics.Scientific reports · 2024Article
- Artificial intelligence in the neonatal intensive care unit: the time is now.Journal of perinatology : official journal of the California Perinatal Association · 2024Review
- Image harmonization and deep learning automated classification of plus disease in retinopathy of prematurity.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- 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
- Multicenter Validation of Deep Learning Algorithm ROP.AI for the Automated Diagnosis of Plus Disease in ROP.Translational vision science & technology · 2023Article
- GabROP: Gabor Wavelets-Based CAD for Retinopathy of Prematurity Diagnosis via Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- Retinopathy of Prematurity in Late Preterm Twins with a Birth Weight Discordance: Can it be Predicted by Artificial Intelligence?Beyoglu eye journal · 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
- Deep Learning Approach in Image Diagnosis ofDiagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study describes the initial development of a deep learning algorithm, ROP.AI, to automatically diagnose retinopathy of prematurity (ROP) plus disease in fundal images.
methodsROP.AI was trained using 6974 fundal images from Australasian image databases. Each image was given a diagnosis as part of real-world routine ROP screening and classified as normal or plus disease. The algorithm was trained using 80% of the images and validated against the remaining 20% within a hold-out test set. Performance in diagnosing plus disease was evaluated against an external set of 90 images. Performance in detecting pre-plus disease was also tested. As a screening tool, the algorithm's operating point was optimized for sensitivity and negative predictive value, and its performance reevaluated.
resultsFor plus disease diagnosis within the 20% hold-out test set, the algorithm achieved a 96.6% sensitivity, 98.0% specificity, and 97.3% ± 0.7% accuracy. Area under the receiver operating characteristic curve was 0.993. Within the independent test set, the algorithm achieved a 93.9% sensitivity, 80.7% specificity, and 95.8% negative predictive value. For detection of pre-plus and plus disease, the algorithm achieved 81.4% sensitivity, 80.7% specificity, and 80.7% negative predictive value. Following the identification of an optimized operating point, the algorithm diagnosed plus disease with a 97.0% sensitivity and 97.8% negative predictive value.
conclusionsROP.AI is a deep learning algorithm able to automatically diagnose ROP plus disease with high sensitivity and negative predictive value. TRANSLATIONAL RELEVANCE: In the context of increasing global disease burden, future development may improve access to ROP diagnosis and care.
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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.