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Updated: May 12, 2026
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Ultrasound Cyclo Plasty in Eyes with Glaucoma
Published on: January 26, 2018
Artificial Intelligence-Guided Surgical Planning in Glaucoma: A Systematic Review Bridging Evidence and Clinical
Vidhya Verma1, Saroj Gupta1, Priti Singh1
1Ophthalmology, All India Institute of Medical Sciences, Bhopal, Bhopal, IND.
Cureus
|May 11, 2026
Summary
Artificial intelligence (AI) significantly enhances glaucoma diagnosis and progression prediction, achieving specialist-level accuracy. This review highlights AI
Area of Science:
- Ophthalmology
- Medical Artificial Intelligence
- Diagnostic Accuracy
Background:
- Glaucoma is a leading cause of irreversible blindness globally.
- Conventional glaucoma diagnosis and monitoring methods suffer from subjectivity and variability.
- Artificial intelligence (AI) offers transformative potential in ophthalmology.
Purpose of the Study:
- To systematically review and synthesize evidence on AI applications in glaucoma.
- To evaluate AI's accuracy in glaucoma diagnosis and progression forecasting.
- To assess AI's role in surgical planning and clinical workflow integration.
Main Methods:
- Systematic review following PRISMA guidelines, with PROSPERO registration.
- Searched PubMed, Embase, Scopus, and Cochrane Library (Jan 2010–Dec 2025).
- Included studies evaluating AI for glaucoma detection, progression, risk stratification, treatment, or surgical support; meta-analysis for diagnostic accuracy (AUC).
Main Results:
- Thirteen studies synthesized, with meta-analysis of diagnostic accuracy from over 10,000 patients.
- AI demonstrated high diagnostic accuracy (pooled AUC 0.93), with multimodal approaches achieving AUC 0.95.
- AI reliably predicted visual field deterioration and showed potential in surgical planning and workflow integration.
Conclusions:
- AI is a powerful tool for glaucoma diagnosis, progression forecasting, and surgical decision-making.
- AI achieves specialist-level diagnostic accuracy, with emerging roles in risk stratification and workflow.
- Future research requires prospective validation, explainable AI, and RCTs for clinical translation.
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