ArticleJAMA ophthalmology2020
Characterization of Central Visual Field Loss in End-stage Glaucoma by Unsupervised Artificial Intelligence.
Article in JAMA ophthalmology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- Dual-Level Pattern Tree for Visual Field Improves Glaucoma Progression and Polygenic Risk Prediction.Translational vision science & technology · 2025Article
- Connecting Visual Perception With Proton Therapy-Induced Optic Damage Using Archetypal Analysis.International journal of particle therapy · 2025Article
- Identification of a Novel Subtype of Ocular Hypertensive Eyes with the Lowest Risk of Progression Under Treatment.Ophthalmology and therapy · 2025Article
- Enhancing central visual field loss representation with a hybrid unsupervised approach.International ophthalmology · 2025Article
- Equity-enhanced glaucoma progression prediction from OCT with knowledge distillation.NPJ digital medicine · 2025Article
- Enhancing diagnostic accuracy in rare and common fundus diseases with a knowledge-rich vision-language model.Nature communications · 2025Article
- Equitable artificial intelligence for glaucoma screening with fair identity normalization.NPJ digital medicine · 2025Article
- Transformer-Based Deep Learning Prediction of 10-Degree Humphrey Visual Field Tests From 24-Degree Data.Translational vision science & technology · 2024Article
- Contributions of Brain Microstructures and Metabolism to Visual Field Loss Patterns in Glaucoma Using Archetypal and Information Gain Analyses.Investigative ophthalmology & visual science · 2024Article
- Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection.Bioengineering (Basel, Switzerland) · 2024Review
- Article
- Uncertainty-inspired open set learning for retinal anomaly identification.Nature communications · 2023Article
- Archetypal analysis of longitudinal visual fields for idiopathic intracranial hypertension patients presenting in a clinic setting.PLOS digital health · 2023Article
- Artificial Intelligence to Aid Glaucoma Diagnosis and Monitoring: State of the Art and New Directions.Photonics · 2022Article
- A Prospective Longitudinal Study to Investigate Corneal Hysteresis as a Risk Factor of Central Visual Field Progression in Glaucoma.American journal of ophthalmology · 2022Article
- Foveal crowding appears to be robust to normal aging and glaucoma unlike parafoveal and peripheral crowding.Journal of vision · 2022Article
- Archetypal Analysis Reveals Quantifiable Patterns of Visual Field Loss in Optic Neuritis.Translational vision science & technology · 2022Article
- The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases.Journal of optometry · 2022Review
- Artificial intelligence improves accuracy, efficiency, and reliability of a handheld infrared eccentric autorefractor for adult refractometry.International journal of ophthalmology · 2022Article
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Authors and funding
16 authors at 12 institutions in 4 countries.
Funding
Abstract
Importance: Although the central visual field (VF) in end-stage glaucoma may substantially vary among patients, structure-function studies and quality-of-life assessments are impeded by the lack of appropriate characterization of end-stage VF loss. Objective: To provide a quantitative characterization and classification of central VF loss in end-stage glaucoma. Design, Setting, and Participants: This retrospective cohort study collected data from 5 US glaucoma services from June 1, 1999, through October 1, 2014. A total of 2912 reliable 10-2 VFs of 1103 eyes from 1010 patients measured after end-stage 24-2 VFs with a mean deviation (MD) of -22 dB or less were included in the analysis. Data were analyzed from March 28, 2018, through May 23, 2019. Main Outcomes and Measures: Central VF patterns were determined by an artificial intelligence algorithm termed archetypal analysis. Longitudinal analyses were performed to investigate whether the development of central VF defect mostly affects specific vulnerability zones. Results: Among the 1103 patients with the most recent VFs, mean (SD) age was 70.4 (14.3) years; mean (SD) 10-2 MD, -21.5 (5.6) dB. Fourteen central VF patterns were determined, including the most common temporal sparing patterns (304 [27.5%]), followed by mostly nasal loss (280 [25.4%]), hemifield loss (169 [15.3%]), central island (120 [10.9%]), total loss (91 [8.3%]), nearly intact field (56 [5.1%]), inferonasal quadrant sparing (42 [3.8%]), and nearly total loss (41 [3.7%]). Location-specific median total deviation analyses partitioned the central VF into a more vulnerable superonasal zone and a less vulnerable inferotemporal zone. At 1-year and 2-year follow-up, new defects mostly occurred in the more vulnerable zone. Initial encroachments on an intact central VF at follow-up were more likely to be from nasal loss (11 [18.4%]; P < .001). One of the nasal loss patterns had a substantial chance at 2-year follow-up (8 [11.0%]; P = .004) to shift to total loss, whereas others did not. Conclusions and Relevance: In this study, central VF loss in end-stage glaucoma was found to exhibit characteristic patterns that might be associated with different subtypes. Initial central VF loss is likely to be nasal loss, and 1 specific type of nasal loss is likely to develop into total loss.
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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.