AI‐Driven Risk Assessment Models for Proactive Glaucoma Monitoring

Glaucoma is one of the main causes of irreversible blindness and requires vision-saving detection and monitoring at early stages. The asymptomatic progression of this condition highlights the need for advanced diagnostic tools. This particular study focuses on AI-based models for early risk assessment of glaucoma and early-stage monitoring through imaging and clinical data integration for personalized healthcare. Our approach integrates four distinct methods: (1) deep learning on OCT images with CNNs by the analysis of structural biomarkers like retinal nerve fiber layer thinning; (2) fundus image classification with transfer learning on pre-trained models to recognize visual field defects and optic nerve damage; (3) predictive analytics on risk factors through individual attributes evaluation, heavy intraocular pressure, age, and family history by gradient-boosted decision trees; and (4) interdisciplinary time-series analysis of intraocular pressure with recurrent neural networks (RNNs) for intraocular pressure fluctuation assessment over time for disease progression. The study used a dataset of 12,000 anonymized patient records, ensuring a diverse representation of demographics and clinical profiles. All incorporated methods achieved more than 95% accuracy, sensitivity, and specificity across all methods. Using these methods, the system successfully stratified patients into low-, moderate-, and high-risk groups for tailored surveillance and treatment. Insight into disease progression was gleaned from time-series analysis, while the predictive capability was further improved through multimodal fusion. This investigation illustrates the profound change possible with AI-enabled glaucoma risk assessment and management, which becomes feasible at scale and low cost. Subsequent work will emphasize clinical validation, integration with electronic health record systems, and the use of data from wearable devices for 24/7 monitoring to formulate individualized treatment strategies.