ArticleOphthalmology2022
Machine-Identified Patterns of Visual Field Loss and an Association with Rapid Progression in the Ocular Hypertension Treatment Study.
Article in Ophthalmology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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.
The trial behind it
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
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- Comparing the Use of Measured and Smoothed Data in Forecasting Visual Field Tests Using Deep Learning.Ophthalmology science · 2026Article
- Potential legal issues arising from the use of artificial intelligence in ophthalmic diagnosis.The Medico-legal journal · 2026Review
- Functional generalized estimating equation model to detect glaucomatous visual field progression.International journal of ophthalmology · 2026Article
- Dual-Level Pattern Tree for Visual Field Improves Glaucoma Progression and Polygenic Risk Prediction.Translational vision science & technology · 2025Article
- Genetic Risk for Open-Angle Glaucoma Subtypes Is Associated with Specific Visual Field Defect Classes.Ophthalmology · 2025Article
- Identification of a Novel Subtype of Ocular Hypertensive Eyes with the Lowest Risk of Progression Under Treatment.Ophthalmology and therapy · 2025Article
- GlaucoRAG: A Retrieval-Augmented Large Language Model for Expert-Level Glaucoma Assessment.medRxiv : the preprint server for health sciences · 2025Article
- Latent retinal structural patterns with aging.NPJ digital medicine · 2025Article
- Testing the Reliability of ChatGPT Assistance for Surgical Choices in Challenging Glaucoma Cases.Journal of personalized medicine · 2025Article
- Large language models as assistance for glaucoma surgical cases: a ChatGPT vs. Google Gemini comparison.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Article
- Machine Learning-Derived Baseline Visual Field Patterns Predict Future Glaucoma Onset in the Ocular Hypertension Treatment Study.Investigative ophthalmology & visual science · 2024Article
- The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports.Ophthalmology and therapy · 2023Article
- Multi-Dataset Comparison of Vision Transformers and Convolutional Neural Networks for Detecting Glaucomatous Optic Neuropathy from Fundus Photographs.Bioengineering (Basel, Switzerland) · 2023Article
- Analysis of volumetric 3D reconstruction of lamina cribrosa images from swept-source optical coherence tomography in glaucomatous and healthy subjects.Biomedical optics express · 2023Article
- Clinical Applications of Artificial Intelligence in Glaucoma.Journal of ophthalmic & vision researchReview
- Application of artificial intelligence in glaucoma care: An updated review.Taiwan journal of ophthalmologyReview
- Big data for imaging assessment in glaucoma.Taiwan journal of ophthalmologyReview
- Big data in visual field testing for glaucoma.Taiwan journal of ophthalmologyReview
- Big data to guide glaucoma treatment.Taiwan journal of ophthalmologyReview
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 1 country.
Funding
Abstract
purposeTo identify patterns of visual field (VF) loss based on unsupervised machine learning and to identify patterns that are associated with rapid progression.
designCross-sectional and longitudinal study.
participantsA total of 2231 abnormal VFs from 205 eyes of 176 Ocular Hypertension Treatment Study (OHTS) participants followed over approximately 16 years.
methodsVisual fields were assessed by an unsupervised deep archetypal analysis algorithm and an OHTS-certified VF reader to identify prevalent patterns of VF loss. Machine-identified patterns of glaucoma damage were compared against those patterns previously identified (expert-identified) in the OHTS in 2003. Based on the longitudinal VFs of each eye, VF loss patterns that were strongly associated with rapid glaucoma progression were identified.
main outcome measuresMachine-expert correspondence and type of patterns of VF loss associated with rapid progression.
resultsThe average VF mean deviation (MD) at conversion to glaucoma was -2.7 decibels (dB) (standard deviation [SD] = 2.4 dB), whereas the average MD of the eyes at the last visit was -5.2 dB (SD = 5.5 dB). Fifty out of 205 eyes had MD rate of -1 dB/year or worse and were considered rapid progressors. Eighteen machine-identified patterns of VF loss were compared with expert-identified patterns, in which 13 patterns of VF loss were similar. The most prevalent expert-identified patterns included partial arcuate, paracentral, and nasal step defects, and the most prevalent machine-identified patterns included temporal wedge, partial arcuate, nasal step, and paracentral VF defects. One of the machine-identified patterns of VF loss predicted future rapid VF progression after adjustment for age, sex, and initial MD.
conclusionsAn automated machine learning system can identify patterns of VF loss and could provide objective and reproducible nomenclature for characterizing early signs of visual defects and rapid progression in patients with glaucoma.
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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.