ArticleThe Journal of clinical investigation2022
A deep-learning system predicts glaucoma incidence and progression using retinal photographs.
Article in The Journal of clinical investigation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 3 of them syntheses that pooled it.
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Who cites it
64 citing papers in PubMed, 3 syntheses or guidelines pooled it, 130 citations in OpenAlex.
- Association Between Polymorphisms of 4 Common Genes and High Myopia Risk: A Comprehensive Analysis.Medical science monitor : international medical journal of experimental and clinical research · 2026Pooled it
- In-depth analysis of research hotspots and emerging trends in AI for retinal diseases over the past decade.Frontiers in medicine · 2024Pooled it
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- Development of a multimodal large language model for early warning and diagnosis of chronic ocular GVHD.NPJ digital medicine · 2026Article
- Establishment of the China Elderly Comorbidity Medical Database (CECMed) and its application in machine learning-based prediction.BMC geriatrics · 2026Article
- Artificial Intelligence in Ophthalmology: Current Status, Challenges, and Future Perspectives.Health care science · 2026Review
- Article
- AI-guided surgical decision support system for corneal laceration repair: a prospective, non-randomized controlled feasibility study.Scientific reports · 2026Article
- A systematic review of AI for predicting glaucoma progression: challenges and recommendations towards clinical implementation.NPJ digital medicine · 2026Article
- Global research status in the treatment of glaucoma: a systematic bibliometric analysis.International journal of ophthalmology · 2026Article
- Oculomics: advances and perspectives from traditional Chinese medicine to modern multimodal biomarkers.International journal of ophthalmology · 2026Review
- An Exploratory Study of Machine Learning-Based Open-Angle Glaucoma Detection Using Specific Autoantibodies.Biomedicines · 2025Article
- Article
- Predictive Utility of Genetic Risk for Myopic Maculopathy Presence and Progression in a Chinese High Myopia Cohort.Investigative ophthalmology & visual science · 2025Article
- Developing and validating an explainable clinlabomics-based machine-learning model for screening primary angle-closure glaucoma in the context of PPPM.The EPMA journal · 2025Article
- Disease Diagnosis Using Retinal Vasculature: Insights from Flammer Syndrome and AI.Brain sciences · 2025Review
- A three-tier AI solution for equitable glaucoma diagnosis across China's hierarchical healthcare system.NPJ digital medicine · 2025Article
- Interpretable longitudinal glaucoma visual field estimation deep learning system from fundus images and clinical narratives.NPJ digital medicine · 2025Article
- Detecting Glaucoma in Highly Myopic Eyes From Fundus Photographs Using Deep Convolutional Neural Networks.Clinical & experimental ophthalmology · 2025Article
- Integrating SEResNet101 and SE-VGG19 for advanced cervical lesion detection: a step forward in precision oncology.BMC cancer · 2025Article
4 more citing papers are in PubMed but not listed here.
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Authors and funding
28 authors at 18 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BackgroundDeep learning has been widely used for glaucoma diagnosis. However, there is no clinically validated algorithm for glaucoma incidence and progression prediction. This study aims to develop a clinically feasible deep-learning system for predicting and stratifying the risk of glaucoma onset and progression based on color fundus photographs (CFPs), with clinical validation of performance in external population cohorts.MethodsWe established data sets of CFPs and visual fields collected from longitudinal cohorts. The mean follow-up duration was 3 to 5 years across the data sets. Artificial intelligence (AI) models were developed to predict future glaucoma incidence and progression based on the CFPs of 17,497 eyes in 9346 patients. The area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity of the AI models were calculated with reference to the labels provided by experienced ophthalmologists. Incidence and progression of glaucoma were determined based on longitudinal CFP images or visual fields, respectively.ResultsThe AI model to predict glaucoma incidence achieved an AUROC of 0.90 (0.81-0.99) in the validation set and demonstrated good generalizability, with AUROCs of 0.89 (0.83-0.95) and 0.88 (0.79-0.97) in external test sets 1 and 2, respectively. The AI model to predict glaucoma progression achieved an AUROC of 0.91 (0.88-0.94) in the validation set, and also demonstrated outstanding predictive performance with AUROCs of 0.87 (0.81-0.92) and 0.88 (0.83-0.94) in external test sets 1 and 2, respectively.ConclusionOur study demonstrates the feasibility of deep-learning algorithms in the early detection and prediction of glaucoma progression.FUNDINGNational Natural Science Foundation of China (NSFC); the High-level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University; the Science and Technology Program of Guangzhou, China (2021), the Science and Technology Development Fund (FDCT) of Macau, and FDCT-NSFC.
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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.