ArticleAmerican journal of ophthalmology2022
Visual Field Endpoints for Neuroprotective Trials: A Case for AI-Driven Patient Enrichment.
Article in American journal of ophthalmology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed.
- Artificial Intelligence-Guided Endpoint Selection for Neuroprotection Trials in Glaucoma.American journal of ophthalmology · 2026Article
- Using the Rate of Global and Pointwise Microperimetry Change to Predict Structural Conversion in Intermediate Age-Related Macular Degeneration.Ophthalmology science · 2026Article
- Artificial intelligence in ophthalmology clinical trials: a narrative review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 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
- Validating Trend-Based End Points for Neuroprotection Trials in Glaucoma.Translational vision science & technology · 2023Article
- Use of artificial intelligence in forecasting glaucoma progression.Taiwan journal of ophthalmologyReview
- Opportunities for Improving Glaucoma Clinical Trials via Deep Learning-Based Identification of Patients with Low Visual Field Variability.Ophthalmology. GlaucomaArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
purposeTo evaluate whether an artificial intelligence (AI) model can better select candidates that would demonstrate visual field (VF) progression, in order to shorten the duration or the number of patients needed for a clinical trial.
designRetrospective cohort study.
methods7428 eyes of 3871 patients from the University of Washington Department of Ophthalmology VF Dataset were included. Progression was defined as at least 5 locations with >7 dB of change compared with baseline on 2 consecutive tests. Progression for all patients, a subgroup of the fastest progressing based on survival curves, and patients selected based on an elastic net Cox regression model were compared. The model was trained on pointwise threshold deviation values of the first VF, age, gender, laterality, and the mean total deviation (MD) at baseline.
resultsA total of 13% of all patients met the criteria for progression at 5 years. Differences in survival were observed when stratified by MD and age (P < .0001). Those at risk of progression included patients aged 60 to 80 years with an initial MD < -5.0. This subgroup decreased the sample size required to detect progression compared with the entire cohort. The AI model-selected patients required the lowest number of patients for all effect sizes and trial lengths. For a trial length of 3 years and effect size of 30%, the number of patients required was 1656 (95% CI, 1638-1674), 903 (95% CI, 884-922), and 636 (95% CI, 625-646) for the entire cohort, the subgroup, and the model-selected patients, respectively.
conclusionAn AI model can identify high-risk patients to substantially reduce the number of patients needed or study duration required to meet clinical trial endpoints.
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