ArticleOphthalmology2020
Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard.
Article in Ophthalmology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- GlaucoRAG: A Retrieval-Augmented Large Language Model for Expert-Level Glaucoma Assessment.medRxiv : the preprint server for health sciences · 2025Article
- A three-tier AI solution for equitable glaucoma diagnosis across China's hierarchical healthcare system.NPJ digital medicine · 2025Article
- Novel Subclassification of Presbyopia Based on Lens Functional and Structural Features.Translational vision science & technology · 2025Observational
- Artificial Intelligence in Glaucoma: Advances in Diagnosis, Progression Forecasting, and Surgical Outcome Prediction.International journal of molecular sciences · 2025Review
- Comparing Ophthalmologist and Artificial Intelligence Chatbot Responses to Patient Questions.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- The use of artificial neural networks in studying the progression of glaucoma.Scientific reports · 2024Article
- Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions.Implementation science : IS · 2024Article
- Predicting 60-4 visual field tests using 3D facial reconstruction.The British journal of ophthalmology · 2023Article
- Performance of ChatGPT in Diagnosis of Corneal Eye Diseases.medRxiv : the preprint server for health sciences · 2023Article
- Comparison of machine learning-based algorithms using corneal asymmetry vs. single-metric parameters for keratoconus detection.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023Article
- Artificial intelligence in ophthalmology: The path to the real-world clinic.Cell reports. Medicine · 2023Review
- Evaluating multidisciplinary glaucoma care: visual field progression and loss of sight year analysis in the community vs hospital setting.Eye (London, England) · 2022Article
- Estimating the Severity of Visual Field Damage From Retinal Nerve Fiber Layer Thickness Measurements With Artificial Intelligence.Translational vision science & technology · 2021Article
- A New Smartphone-Based Optic Nerve Head Biometric for Verification and Change Detection.Translational vision science & technology · 2021Article
- A deep learning approach to identify blepharoptosis by convolutional neural networks.International journal of medical informatics · 2021Article
- Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice.Translational vision science & technology · 2020Article
- An Artificial Intelligence Enabled System for Retinal Nerve Fiber Layer Thickness Damage Severity Staging.Ophthalmology scienceArticle
- Use of artificial intelligence in forecasting glaucoma progression.Taiwan journal of ophthalmologyReview
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
purposeTo develop an artificial intelligence (AI) dashboard for monitoring glaucomatous functional loss.
designRetrospective, cross-sectional, longitudinal cohort study.
participantsOf 31 591 visual fields (VFs) on 8077 subjects, 13 231 VFs from the most recent visit of each patient were included to develop the AI dashboard. Longitudinal VFs from 287 eyes with glaucoma were used to validate the models.
methodWe entered VF data from the most recent visit of glaucomatous and nonglaucomatous patients into a "pipeline" that included principal component analysis (PCA), manifold learning, and unsupervised clustering to identify eyes with similar global, hemifield, and local patterns of VF loss. We visualized the results on a map, which we refer to as an "AI-enabled glaucoma dashboard." We used density-based clustering and the VF decomposition method called "archetypal analysis" to annotate the dashboard. Finally, we used 2 separate benchmark datasets-one representing "likely nonprogression" and the other representing "likely progression"-to validate the dashboard and assess its ability to portray functional change over time in glaucoma.
main outcome measuresThe severity and extent of functional loss and characteristic patterns of VF loss in patients with glaucoma.
resultsAfter building the dashboard, we identified 32 nonoverlapping clusters. Each cluster on the dashboard corresponded to a particular global functional severity, an extent of VF loss into different hemifields, and characteristic local patterns of VF loss. By using 2 independent benchmark datasets and a definition of stability as trajectories not passing through over 2 clusters in a left or downward direction, the specificity for detecting "likely nonprogression" was 94% and the sensitivity for detecting "likely progression" was 77%.
conclusionsThe AI-enabled glaucoma dashboard, developed using a large VF dataset containing a broad spectrum of visual deficit types, has the potential to provide clinicians with a user-friendly tool for determination of the severity of glaucomatous vision deficit, the spatial extent of the damage, and a means for monitoring the disease progression.
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