ArticleTranslational vision science & technology2021
Deep-Learning-Based Pre-Diagnosis Assessment Module for Retinal Photographs: A Multicenter Study.
Article in Translational vision science & technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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
13 citing papers in PubMed, 16 citations in OpenAlex.
- Diagnostic Role of Multimodal Imaging in Optic Disc Diseases.Diagnostics (Basel, Switzerland) · 2026Review
- Generative adversarial networks (GAN) for pre-dilation retinal photograph quality enhancement.Eye (London, England) · 2026Article
- Automated anatomically guided quality assessment for OCTA via multi-region analysis and statistical calibration.Biomedical optics express · 2026Article
- High-accuracy retinal age prediction via fundus-based multitask learning reveals the effect of systemic disease.Communications medicine · 2026Article
- The acceptance of ophthalmic artificial intelligence for eye diseases: a literature review and qualitative analysis.Eye (London, England) · 2025Review
- Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection.Translational vision science & technology · 2024Article
- A Deep Learning System for Automated Quality Evaluation of Optic Disc Photographs in Neuro-Ophthalmic Disorders.Diagnostics (Basel, Switzerland) · 2023Article
- Automated image curation in diabetic retinopathy screening using deep learning.Scientific reports · 2022Article
- Assessment of image quality on color fundus retinal images using the automatic retinal image analysis.Scientific reports · 2022Article
- Early Diagnosis and Quantitative Analysis of Stages in Retinopathy of Prematurity Based on Deep Convolutional Neural Networks.Translational vision science & technology · 2022Article
- The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases.Journal of optometry · 2022Review
- Highly Accurate and Precise Automated Cup-to-Disc Ratio Quantification for Glaucoma Screening.Ophthalmology scienceArticle
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Corrections and comments
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Authors and funding
17 authors at 7 institutions in 5 countries.
Funding
Abstract
Purpose: Artificial intelligence (AI) deep learning (DL) has been shown to have significant potential for eye disease detection and screening on retinal photographs in different clinical settings, particular in primary care. However, an automated pre-diagnosis image assessment is essential to streamline the application of the developed AI-DL algorithms. In this study, we developed and validated a DL-based pre-diagnosis assessment module for retinal photographs, targeting image quality (gradable vs. ungradable), field of view (macula-centered vs. optic-disc-centered), and laterality of the eye (right vs. left). Methods: A total of 21,348 retinal photographs from 1914 subjects from various clinical settings in Hong Kong, Singapore, and the United Kingdom were used for training, internal validation, and external testing for the DL module, developed by two DL-based algorithms (EfficientNet-B0 and MobileNet-V2). Results: For image-quality assessment, the pre-diagnosis module achieved area under the receiver operating characteristic curve (AUROC) values of 0.975, 0.999, and 0.987 in the internal validation dataset and the two external testing datasets, respectively. For field-of-view assessment, the module had an AUROC value of 1.000 in all of the datasets. For laterality-of-the-eye assessment, the module had AUROC values of 1.000, 0.999, and 0.985 in the internal validation dataset and the two external testing datasets, respectively. Conclusions: Our study showed that this three-in-one DL module for assessing image quality, field of view, and laterality of the eye of retinal photographs achieved excellent performance and generalizability across different centers and ethnicities. Translational Relevance: The proposed DL-based pre-diagnosis module realized accurate and automated assessments of image quality, field of view, and laterality of the eye of retinal photographs, which could be further integrated into AI-based models to improve operational flow for enhancing disease screening and diagnosis.
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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.