ArticleTranslational vision science & technology2022
UWHVF: A Real-World, Open Source Dataset of Perimetry Tests From the Humphrey Field Analyzer at the University of Washington.
Article in Translational vision science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Toward Long-Term Visual Field Appearance Forecasting Using Artificial Intelligence for Ophthalmic Education and Diagnosis.Ophthalmology science · 2026Article
- PyVisualFields Version 2: From an R Wrapper to a Comprehensive Python Ecosystem for Visual Field and Glaucoma Research.Translational vision science & technology · 2026Article
- Predicting worsening visual field via masked dual stage attention recurrent neural network.Scientific reports · 2026Article
- Artificial Intelligence-Guided Endpoint Selection for Neuroprotection Trials in Glaucoma.American journal of ophthalmology · 2026Article
- A Structure-Function Dataset With Known Rates of Glaucomatous Progression.Translational vision science & technology · 2026Article
- Reliable and Interpretable Visual Field Progression Prediction with Diffusion Models and Conformal Risk Control.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- [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
- A multi-label transformer-based deep learning approach to predict focal visual field progression.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Article
- GRAPE: A multi-modal dataset of longitudinal follow-up visual field and fundus images for glaucoma management.Scientific data · 2023Article
- Visual Field Endpoints for Neuroprotective Trials: A Case for AI-Driven Patient Enrichment.American journal of ophthalmology · 2022Article
- Development of an Open-Source Annotated Glaucoma Medication Dataset From Clinical Notes in the Electronic Health Record.Translational vision science & technology · 2022Article
- The Open Perimetry Initiative: A framework for cross-platform development for the new generation of portable perimeters.Journal of vision · 2022Article
- Use of artificial intelligence in forecasting glaucoma progression.Taiwan journal of ophthalmologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Purpose: This article describes the Humphrey field analyzer (HFA) dataset from the Department of Ophthalmology at the University of Washington. Methods: Pointwise sensitivities were extracted from HFA 24-2, stimulus III visual fields (VF). Total deviation (TD), mean TD (MTD), pattern deviation, and pattern standard deviation (PSD) were calculated. Progression analysis was performed with simple linear regression on global, regional, and pointwise values for VF series with greater than four tests spanning at least four months. VF data were extracted independently of clinical information except for patient age, gender, and laterality. Results: This dataset includes 28,943 VFs from 7248 eyes of 3871 patients. Progression was calculated for 2985 eyes from 1579 patients. Median [interquartile range] age was 64 years [54, 73], and follow-up was 2.49 years [1.11, 5.03]. Baseline MTD was -4.51 dB [-8.01, -2.65], and baseline PSD was 2.41 dB [1.7, 5.34]. Conclusion: MTD was found to decrease by -0.10 dB/yr [-0.40, 0.11] in eyes for which progression analysis was able to be performed. VFs with deep localized defects, PSD > 12 dB and MTD -15 dB to -25 dB, were plotted, visually inspected, and found to be consistent with neurologic or glaucomatous VFs from patients. For a small number of tests, extracted sensitivity values were compared to corresponding printouts and confirmed to match. Translational Relevance: This open access pointwise VF dataset serves as a source of raw data for investigation such as VF behavior, clinical comparisons to trials, and development of new machine learning algorithms.
Indexed as
Identifiers
What OpenQuestion holds
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.