ArticleOphthalmology2019
Agreement and Predictors of Discordance of 6 Visual Field Progression Algorithms.
Article in Ophthalmology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 35 citations in OpenAlex.
- Unsupervised Classification of Wide-Field Swept-Source Optical Coherence Tomography Reveals Patterns Associated With Glaucoma Severity and Progression.Translational vision science & technology · 2026Article
- Labeling matters: A multicenter machine learning study on visual field progression in Glaucoma.PloS one · 2026Article
- Two-Stage Decoupling Framework for Variable-Length Glaucoma Prognosis.Learning with longitudinal medical images and data : first International Workshop, LMID 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 27, 2025, Proceedings. International Workshop on Learning with Longitudi... · 2026Article
- Comparison of Methods for Visual Field Progression in Eyes With Central Visual Field Defects.Translational vision science & technology · 2025Article
- [Visual field prediction based on temporal-spatial feature learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Frequency of Agreement Between Structural and Functional Glaucoma Testing: A Longitudinal Study of 3D OCT and Current Clinical Tests.American journal of ophthalmology · 2024Article
- GRAPE: A multi-modal dataset of longitudinal follow-up visual field and fundus images for glaucoma management.Scientific data · 2023Article
- Predicting Visual Field Worsening with Longitudinal OCT Data Using a Gated Transformer Network.Ophthalmology · 2023Article
- Combining Optical Coherence Tomography and Optical Coherence Tomography Angiography Longitudinal Data for the Detection of Visual Field Progression in Glaucoma.American journal of ophthalmology · 2023Article
- A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach.Scientific reports · 2023Article
- Artificial Intelligence to Aid Glaucoma Diagnosis and Monitoring: State of the Art and New Directions.Photonics · 2022Article
- Multilayer Macula Vessel Density and Visual Field Progression in Glaucoma.American journal of ophthalmology · 2022Article
- Archetypal Analysis Reveals Quantifiable Patterns of Visual Field Loss in Optic Neuritis.Translational vision science & technology · 2022Article
- Unsupervised Machine Learning Identifies Quantifiable Patterns of Visual Field Loss in Idiopathic Intracranial Hypertension.Translational vision science & technology · 2021Article
- Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data.Ophthalmology · 2021Article
- Development and Comparison of Machine Learning Algorithms to Determine Visual Field Progression.Translational vision science & technology · 2021Article
- Prediction of Visual Field Progression from OCT Structural Measures in Moderate to Advanced Glaucoma.American journal of ophthalmology · 2021Article
- Quantification of Retinal Ganglion Cell Morphology in Human Glaucomatous Eyes.Investigative ophthalmology & visual science · 2021Article
- Development and Validation of Automated Visual Field Report Extraction Platform Using Computer Vision Tools.Frontiers in medicine · 2021Article
- Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice.Translational vision science & technology · 2020Article
Corrections and comments
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Authors and funding
14 authors at 7 institutions in 1 country.
Funding
Abstract
purposeTo determine the agreement of 6 established visual field (VF) progression algorithms in a large dataset of VFs from multiple institutions and to determine predictors of discordance among these algorithms.
designRetrospective longitudinal cohort study.
participantsVisual fields from 5 major eye care institutions in the United States were analyzed, including a subset of eyes with at least 5 Swedish interactive threshold algorithm standard 24-2 VFs that met our reliability criteria. Of a total of 831 240 VFs, a subset of 90 713 VFs from 13 156 eyes of 8499 patients met the inclusion criteria.
methodsSix commonly used VF progression algorithms (mean deviation [MD] slope, VF index slope, Advanced Glaucoma Intervention Study, Collaborative Initial Glaucoma Treatment Study, pointwise linear regression, and permutation of pointwise linear regression) were applied to this cohort, and each eye was determined to be stable or progressing using each measure. Agreement between individual algorithms was tested using Cohen's κ coefficient. Bivariate and multivariate analyses were used to determine predictors of discordance (3 algorithms progressing and 3 algorithms stable).
main outcome measuresAgreement and discordance between algorithms.
resultsIndividual algorithms showed poor to moderate agreement with each other when compared directly (κ range, 0.12-0.52). Based on at least 4 algorithms, 11.7% of eyes progressed. Major predictors of discordance or lack of agreement among algorithms were more depressed initial MD (P < 0.01) and older age at first available VF (P < 0.01). A greater number of VFs (P < 0.01), more years of follow-up (P < 0.01), and eye care institution (P = 0.03) also were associated with discordance.
conclusionsThis extremely large comparative series demonstrated that existing algorithms have limited agreement and that agreement varies with clinical parameters, including institution. These issues underscore the challenges to the clinical use and application of progression algorithms and of applying big-data results to individual practices.
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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.