ArticleEye (London, England)2026
Deep learning algorithms for timely diagnosis of retinopathy of prematurity requiring treatment.
Article in Eye (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Enhancing deep learning telemedicine for retinopathy of prematurity: current evidence and opportunities for improvement.Eye (London, England) · 2026Article
- VEA-net: vascular enhancement attention with dual-backbone multi-task learning for comprehensive ROP management across multi-center datasets.Frontiers in cell and developmental biology · 2026Article
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Authors and funding
10 authors.
Funding
Abstract
BACKGROUND/
objectivesTo evaluate the effectiveness of deep learning (DL) algorithms in diagnosing Retinopathy of Prematurity (ROP) cases that requires treatment using fundus images submitted to the ROP clinic as part of a telemedicine consultation system. SUBJECTS/
methodsThis retrospective cross-sectional study analysed 1700 RetCam fundus images from 141 preterm infants screened for ROP at Khatam-Al-Anbia Eye Hospital. The images underwent preprocessing using Contrast Limited Adaptive Histogram Equalisation (CLAHE), Automated Multiscale Retinex (AMSR), and a machine learning-based optimisation approach (ML). Various convolutional neural network (CNN) models such as MobileNet, ResNet-18, ResNet-50, and DenseNet-121, were evaluated for their diagnostic performance utilising accuracy, sensitivity, specificity, and F1-score metrics.
resultsAmong the models tested, MobileNet with CLAHE preprocessing achieved the highest accuracy (91.39%) and sensitivity (94.90%), establishing it as the most effective model for ROP detection. DenseNet-121 with CLAHE preprocessing showcased high sensitivity (94.26%) but slightly lower accuracy (90.98%). Additionally, ResNet-50 with AMSR preprocessing also demonstrated high accuracy (90.58%) and sensitivity (91.44%). These findings underscore the feasibility of DL models for real-time ROP screening in telemedicine environments.
conclusionMobileNet with CLAHE preprocessing exhibited the highest diagnostic performance in identifying treatment-requiring ROP, positioning it as a promising tool for AI-assisted screening. Further validation in varied clinical settings is necessary to confirm its real-world applicability.
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