ArticleTranslational vision science & technology2022
Predictive Modeling of Long-Term Glaucoma Progression Based on Initial Ophthalmic Data and Optic Nerve Head Characteristics.
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 8 papers.
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Who cites it
8 citing papers in PubMed.
- A systematic review of AI for predicting glaucoma progression: challenges and recommendations towards clinical implementation.NPJ digital medicine · 2026Article
- A Comprehensive Review of the Clinical Evidence Comparing Benzalkonium Chloride-Preserved and Benzalkonium Chloride-Free Latanoprost in the Treatment of Primary Open-Angle Glaucoma and Ocular Hypertension.Clinical ophthalmology (Auckland, N.Z.) · 2026Review
- Predicting Intraocular Pressure From Glaucoma Patients Receiving Medication Treatment Using Explainable Machine Learning.BioMed research international · 2026Article
- Integrating Retinal Segmentation Metrics with Machine Learning for Predictions from Mouse SD-OCT Scans.Current eye research · 2025Article
- AI-Based Ocular Age Estimation from Combined OCT and OCTA Metrics: Decade-Stratified Normative Modelling in Healthy Eyes - A Pilot Study [Letter].Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- AI-Based Ocular Age Estimation from Combined OCT and OCTA Metrics: Decade-Stratified Normative Modelling in Healthy Eyes - a Pilot Study.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Use of artificial intelligence in forecasting glaucoma progression.Taiwan journal of ophthalmologyReview
- Application of artificial intelligence in glaucoma care: An updated review.Taiwan journal of ophthalmologyReview
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The purpose of this study was to develop a model, based on initial optic nerve head (ONH) characteristics, predictive of long-term rapid retinal nerve fiber layer (RNFL) thinning in patients with open-angle glaucoma (OAG). Methods: This study evaluated 712 eyes with OAG that had been followed up for >5 years with annual evaluation of RNFL thickness. Baseline ophthalmic features were incorporated into the machine learning models for prediction of faster RNFL thinning. The model was trained and tested using a random forest (RF) method, and was interpreted using Shapley additive explanations. Factors associated with faster rate of RNFL thinning were statistically evaluated using a decision tree. Results: The RF model showed that greater lamina cribrosa (LC) curvature, higher intraocular pressure (IOP), visual field mean deviation converging towards -5 dB, and thinner peripapillary choroid at baseline were the four most significant features predicting faster RNFL thinning. Partial interaction between the features showed that larger LC curvature was a strong factor for faster RNFL thinning when it exceeded approximately 12.0. When the LC curvature was ≤12, higher initial IOP and thinner peripapillary choroid played a role in the rapid RNFL thinning. Based on the decision tree, higher IOP (>26.5 mm Hg), greater laminar curvature (>13.95), and thinner peripapillary choroid (≤117.5 µm) were the 3 most important determinants affecting the rate of RNFL thinning. Conclusions: Baseline ophthalmic data and ONH characteristics of patients with OAG were predictive of eyes at risk of faster progression. Combinations of important characteristics, such as IOP, LC curvature, and choroidal thickness, could stratify eyes into groups with different rates of RNFL thinning. Translational Relevance: This work lays the foundations for developing prediction models to estimate glaucoma prognosis based on initial ONH characteristics.
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