AI Supports Glaucoma Surgical Planning

Predictive models identify high-risk patients and support earlier treatment decisions.

Category	Detail
Condition	Glaucoma
Key Mechanisms	Artificial intelligence models using multimodal imaging and clinical data to diagnose, predict progression, and support surgical decision-making
Target Population	Patients with glaucoma undergoing diagnostic evaluation and surgical planning
Care Setting	Ophthalmology clinical settings integrating AI decision support systems

AI diagnostic models achieved high accuracy with pooled AUC of 0.93 across >64,000 images and >10,000 patients
Multimodal AI models integrating fundus photography, OCT, and visual field data outperform single-modality systems
AI predicts visual field deterioration up to 4 years in advance, aiding timely surgical intervention and risk stratification

Utilize AI models, especially multimodal deep-learning systems, to enhance glaucoma diagnostic accuracy
Incorporate structural imaging segmentation of optic nerve head and lamina cribrosa for presurgical assessment

Apply AI-based progression and risk prediction models to identify fast progressors and guide surgical versus medical therapy decisions
Integrate AI decision support tools into clinical workflows to optimize referral pathways and surgical triaging

Use AI to forecast visual field deterioration and monitor disease progression longitudinally
Leverage multimodal data including electronic health records for ongoing risk stratification

Be aware of limitations including retrospective study designs, methodological heterogeneity, and limited external validation
Consider variability in AI model reproducibility and incomplete access to underlying code and data sets

Glaucoma patients evaluated for disease progression and surgical candidacy

AI models can distinguish patients suitable for continued medical therapy versus those requiring surgical escalation based on risk stratification

Employ multimodal AI approaches combining fundus photography, OCT, and visual field data for superior diagnostic performance
Incorporate AI-driven structural imaging analysis to support presurgical planning
Use AI predictions of progression to enable early identification of fast progressors and timely surgical intervention
Integrate AI decision support systems into routine clinical workflows to enhance surgical planning and patient triage