ArticleOphthalmology. Glaucoma
Forecasting Risk of Future Rapid Glaucoma Worsening Using Early Visual Field, OCT, and Clinical Data.
Article in Ophthalmology. Glaucoma. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 19 citations in OpenAlex.
- Dynamic Oculomics for Diagnostic Monitoring: Personal Ocular Trends, Risk Stratification and Treatment Response.Diagnostics (Basel, Switzerland) · 2026Review
- Detecting dataset bias in medical AI using a generalized and modality agnostic auditing approach.NPJ digital medicine · 2026Article
- A systematic review of AI for predicting glaucoma progression: challenges and recommendations towards clinical implementation.NPJ digital medicine · 2026Article
- Development and Evaluation of an Artificial Intelligence Model to Set Target IOP for Glaucoma.American journal of ophthalmology · 2025Article
- The explainable AI dilemma under knowledge imbalance in specialist AI for glaucoma referrals in primary care.NPJ digital medicine · 2025Article
- Augmented Decisions: AI-Enhanced Accuracy in Glaucoma Diagnosis and Treatment.Journal of clinical medicine · 2025Review
- Detecting glaucoma worsening using optical coherence tomography derived visual field estimates.Scientific reports · 2025Article
- 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
- Deep learning-based identification of eyes at risk for glaucoma surgery.Scientific reports · 2024Article
- Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications.Frontiers in ophthalmology · 2023Article
- Big data in visual field testing for glaucoma.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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo assess whether we can forecast future rapid visual field (VF) worsening using deep learning models (DLMs) trained on early VF, OCT, and clinical data.
designA retrospective cohort study. SUBJECTS: In total, 4536 eyes from 2962 patients. Overall, 263 (5.80%) eyes underwent rapid VF worsening (mean deviation slope less than -1 dB/year across all VFs).
methodsWe included eyes that met the following criteria: (1) followed for glaucoma or suspect status; (2) had at least 5 longitudinal reliable VFs (VF
main outcome measuresThe AUC of DLMs when forecasting rapidly worsening eyes.
resultsModel V
conclusionsDeep learning models can forecast future rapid glaucoma worsening with modest to high performance when trained using data from early in the disease course. Including baseline data from multiple modalities and subsequent visits improves performance beyond using VF data alone. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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.