ArticleBMJ open2025
Deep learning-based automatic image quality assessment in ultra-widefield fundus photographs.
Article in BMJ open, 2025. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- From discarding to leveraging: quality-aware collaborative learning for robust diabetic retinopathy grading.Frontiers in medicine · 2026Article
- Deep learning-based no-reference image quality assessment framework for Cryptosporidium spp. and Giardia spp.PloS one · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWith a growing need for ultra-widefield fundus (UWF) fundus photographs in clinics and AI development, image quality assessment (IQA) of UWF fundus photographs is an important preceding step for accurate diagnosis and clinical interpretation. This study developed deep learning (DL) models for automated IQA of UWF fundus photographs (UWF-IQA model) and investigated intergrader agreements in the IQA of UWF fundus photographs. METHODS AND ANALYSIS: We included 4749 UWF images of 2124 patients to set the UWF-IQA dataset. Three independent board-certified ophthalmologists manually assessed each UWF image on four grading criteria (field of view, peripheral visualisation, details of posterior pole and centring of the image) and a final IQA grading using a five-point scale. The UWF-IQA model was developed to predict IQA scores with EfficientNet-B3 as the backbone model. For the test dataset, Cohen's quadratic weighted kappa score was calculated to evaluate intergrader agreements and agreements between predicted IQA scores and manual gradings.
resultsDevelopment and test dataset consisted of 3790 images from 1699 patients and 959 images of 425 patients, respectively, without statistical differences in IQA gradings. The average agreement between the UWF-IQA model and manual graders was 0.731, while the average of intergrader agreements among manual graders was 0.603 (Cohen's weighted kappa score). Posterior pole grading showed the highest average agreements (0.838) between the UWF-IQA model and manual graders, followed by final grading (0.788), centring of the image (0.754), peripheral visualisation (0.754) and field of view (0.535).
conclusionPredicted IQA scores using the UWF-IQA model showed better agreements with manual graders compared with intergrader agreements. The automated UWF-IQA model offers robust and efficient IQA predictions with the final and subcategory gradings.
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Registered trials
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