ArticleAmerican journal of ophthalmology2021
Automated Detection of Glaucoma With Interpretable Machine Learning Using Clinical Data and Multimodal Retinal Images.
Article in American journal of ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 2 of them syntheses that pooled it.
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Who cites it
44 citing papers in PubMed, 2 syntheses or guidelines pooled 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
- Explainable Multimodal OCT and OCTA Analysis Reveals Structural and Vascular Features of Mild Glaucoma Across Ethnicities.Investigative ophthalmology & visual science · 2026Observational
- Artificial Intelligence-Driven Multimodal Prediction of 10-Year Incident Glaucoma Integrating Genetic and Deep Learning-Derived Imaging Features.Ophthalmology science · 2026Article
- Multimodal models based on radiomics and deep learning in the classification of gastric stromal tumors and gastric leiomyomas.Surgical endoscopy · 2026Article
- Article
- Multimodal deep learning approaches for improving polygenic risk scores with imaging data.Scientific reports · 2026Article
- Multimodal Integration in Health Care: Development With Applications in Disease Management.Journal of medical Internet research · 2025Review
- Diagnostic Accuracy of 3D Deep Learning Classifiers for Glaucoma Detection: A Comparison of Cross-Domain and Device-Specific Models.Translational vision science & technology · 2025Article
- Novel Deep Learning Model for Glaucoma Detection Using Fusion of Fundus and Optical Coherence Tomography Images.Sensors (Basel, Switzerland) · 2025Article
- A three-tier AI solution for equitable glaucoma diagnosis across China's hierarchical healthcare system.NPJ digital medicine · 2025Article
- Relationship between intraocular pressure and pulmonary function.Scientific reports · 2025Article
- OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning.Scientific reports · 2025Article
- Research on interdisciplinary measurement and evolutionary path in the field of ophthalmology.Frontiers in ophthalmology · 2025Article
- Advances and prospects of multi-modal ophthalmic artificial intelligence based on deep learning: a review.Eye and vision (London, England) · 2024Review
- Prediction of Post-Treatment Visual Acuity in Age-Related Macular Degeneration Patients With an Interpretable Machine Learning Method.Translational vision science & technology · 2024Article
- Integrating Deep Learning with Electronic Health Records for Early Glaucoma Detection: A Multi-Dimensional Machine Learning Approach.Bioengineering (Basel, Switzerland) · 2024Article
- Assessing the efficacy of 2D and 3D CNN algorithms in OCT-based glaucoma detection.Scientific reports · 2024Article
- Convolutional neural network-based classification of glaucoma using optic radiation tissue properties.Communications medicine · 2024Article
- Explainable machine learning predictions of perceptual sensitivity for retinal prostheses.Journal of neural engineering · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
purposeTo develop a multimodal model to automate glaucoma detection
designDevelopment of a machine-learning glaucoma detection model
methodsWe selected a study cohort from the UK Biobank data set with 1193 eyes of 863 healthy subjects and 1283 eyes of 771 subjects with glaucoma. We trained a multimodal model that combines multiple deep neural nets, trained on macular optical coherence tomography volumes and color fundus photographs, with demographic and clinical data. We performed an interpretability analysis to identify features the model relied on to detect glaucoma. We determined the importance of different features in detecting glaucoma using interpretable machine learning methods. We also evaluated the model on subjects who did not have a diagnosis of glaucoma on the day of imaging but were later diagnosed (progress-to-glaucoma [PTG]).
resultsResults show that a multimodal model that combines imaging with demographic and clinical features is highly accurate (area under the curve 0.97). Interpretation of this model highlights biological features known to be related to the disease, such as age, intraocular pressure, and optic disc morphology. Our model also points to previously unknown or disputed features, such as pulmonary function and retinal outer layers. Accurate prediction in PTG highlights variables that change with progression to glaucoma-age and pulmonary function.
conclusionsThe accuracy of our model suggests distinct sources of information in each imaging modality and in the different clinical and demographic variables. Interpretable machine learning methods elucidate subject-level prediction and help uncover the factors that lead to accurate predictions, pointing to potential disease mechanisms or variables related to the disease.
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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.