Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence

Area of Science:

• Ophthalmology
• Medical Imaging
• Artificial Intelligence

Background:

• Glaucoma is a leading cause of irreversible blindness worldwide, stemming from retinal ganglion cell neurodegeneration.
• Current diagnostic methods like OCT and visual field exams are time-consuming and subjective.
• AI, including ML and DL, offers a powerful tool for rapid, accurate analysis of glaucoma imaging data.

Purpose of the Study:

• To review current AI applications in glaucoma diagnosis, treatment, and detection.
• To evaluate the strengths and weaknesses of various AI models and algorithms.
• To explore AI's potential to transform glaucoma care and identify future research directions.

Main Methods:

• Comprehensive literature search of major databases (Web of Science, PubMed, Scopus) up to August 2024.
• Inclusion of peer-reviewed English articles focusing on AI, ML, or DL for human glaucoma diagnosis/treatment.
• Selection based on quality, contribution, and relevance to AI in glaucoma.

Main Results:

• Deep learning algorithms, particularly CNNs, demonstrate high sensitivity and specificity in identifying glaucoma from fundus photos, OCT, and visual field tests.
• AI models show potential in automating screening, differentiating between healthy and glaucomatous eyes, and predicting disease progression.
• Challenges include the need for diverse datasets, external validation, transparent decision-making, and addressing ethical/legal concerns.

Conclusions:

• AI offers significant potential to enhance glaucoma diagnosis and treatment through precise image analysis and clinical decision support.
• Future research must focus on developing generalizable, validated AI models across diverse populations.
• Addressing ethical/legal issues and integrating AI into clinical practice are crucial for improved patient outcomes.