ArticleEye (London, England)2024
Automated detection of type 1 ROP, type 2 ROP and A-ROP based on deep learning.
Article in Eye (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Healthcare analytics perspectives on artificial intelligence-based retinopathy of prematurity screening: a bibliometric mapping study.Frontiers in research metrics and analytics · 2026Pooled it
- Optimizing Field-of-View for Type-1 Retinopathy of Prematurity via Multiple Instance Learning.Translational vision science & technology · 2026Article
- Artificial Intelligence Applications in Sickle Cell Retinopathy Imaging: Current Progress, Challenges, and Future Directions.Journal of ophthalmology · 2026Review
- Automated diagnosis of early-stage retinopathy of prematurity.Eye (London, England) · 2026Article
- Deep learning algorithms for timely diagnosis of retinopathy of prematurity requiring treatment.Eye (London, England) · 2026Article
- Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifier.MethodsX · 2025Article
- An Interpretable System for Screening the Severity Level of Retinopathy in Premature Infants Using Deep Learning.Bioengineering (Basel, Switzerland) · 2024Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo provide automatic detection of Type 1 retinopathy of prematurity (ROP), Type 2 ROP, and A-ROP by deep learning-based analysis of fundus images obtained by clinical examination using convolutional neural networks. MATERIAL AND
methodsA total of 634 fundus images of 317 premature infants born at 23-34 weeks of gestation were evaluated. After image pre-processing, we obtained a rectangular region (ROI). RegNetY002 was used for algorithm training, and stratified 10-fold cross-validation was applied during training to evaluate and standardize our model. The model's performance was reported as accuracy and specificity and described by the receiver operating characteristic (ROC) curve and area under the curve (AUC).
resultsThe model achieved 0.98 accuracy and 0.98 specificity in detecting Type 2 ROP versus Type 1 ROP and A-ROP. On the other hand, as a result of the analysis of ROI regions, the model achieved 0.90 accuracy and 0.95 specificity in detecting Stage 2 ROP versus Stage 3 ROP and 0.91 accuracy and 0.92 specificity in detecting A-ROP versus Type 1 ROP. The AUC scores were 0.98 for Type 2 ROP versus Type 1 ROP and A-ROP, 0.85 for Stage 2 ROP versus Stage 3 ROP, and 0.91 for A-ROP versus Type 1 ROP.
conclusionOur study demonstrated that ROP classification by DL-based analysis of fundus images can be distinguished with high accuracy and specificity. Integrating DL-based artificial intelligence algorithms into clinical practice may reduce the workload of ophthalmologists in the future and provide support in decision-making in the management of ROP.
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