Glaucoma, a leading cause of irreversible blindness, is a complex, multifaceted disease. Diagnosis relies on evaluating a handful of factors, including your internal eye pressure, nerve fiber defects, and visual field changes. Given the vast amount of clinical data required for a comprehensive diagnosis, artificial intelligence (AI) and machine learning (ML) are increasingly being explored as tools to enhance glaucoma detection, treatment, and prognosis. Learn more about how AI can help improve outcomes for glaucoma patients.*

Definitions in AI 

AI encompasses a range of techniques designed to mimic human intelligence, particularly in pattern recognition and decision-making. Several key subfields contribute to AI’s growing role in medical imaging and diagnostics.

• Artificial intelligence (AI). Broadly refers to computer systems designed to perform tasks that require human intelligence
• Machine learning (ML). A subset of AI that enables computers to learn patterns from data without being explicitly programmed
• Neural networks (NN). Computational models inspired by the human brain, consisting of interconnected nodes that process data through multiple layers
• Deep learning (DL). A specialized ML technique using multiple NN layers to extract featured data and enhance image processing (ex: of imaging test scans)

How AI Can Be Applied to Glaucoma 

AI and ML have proven useful in glaucoma prevention and management, so far, in several ways. They have been used to:

• Analyze data from continuous intraocular pressure (IOP) monitoring devices, to identify trends and assess glaucoma risk
• Achieve high levels of accuracy in detecting glaucoma-related optic neuropathy, with some systems outperforming human ophthalmologists in certain diagnostic tasks
• Analyze retinal nerve fiber layer and ganglion cell layer thickness for more accurate glaucoma detection (with accuracy rates exceeding 90%)
• Achieve nearly 90% accuracy in identifying angle-closure glaucoma from AS-OCT scans
• Help distinguish glaucomatous from non-glaucomatous visual field test results.
• Forecast disease progression up to 5.5 years in advance

Looking Ahead 

The future of AI in glaucoma lies in developing diagnostic systems that bring together multiple sets of data into a single model. Emerging AI techniques could personalize treatment strategies by forecasting disease progression, streamlining diagnosis, allowing for earlier intervention, and improving patient outcomes. 

*David Akkara, J., Kuriakose, A., Brown, E. N., Moore, D. B. et al. (2024, December 2). Artificial Intelligence in Glaucoma. EyeWiki. https://eyewiki.org/Artificial_Intelligence_in_Glaucoma 

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