Artificial Intelligence Application in Cornea and External Diseases.

Area of Science:

• Ophthalmology
• Medical Imaging
• Artificial Intelligence

Background:

• Corneal diseases are a major cause of global blindness.
• Early detection is difficult due to subtle clinical signs.
• Artificial intelligence (AI) offers potential for improved diagnosis of anterior segment disorders.

Purpose of the Study:

• To review current applications of AI in detecting corneal conditions.
• To assess the diagnostic performance of AI models for corneal diseases.
• To explore AI's role in image labeling, education, and patient monitoring.

Main Methods:

• Narrative review of AI applications in corneal disease detection.
• Analysis of AI model performance on test datasets.
• Inclusion of conditions like keratoconus, dry eye, infectious keratitis, pterygium, Fuchs endothelial corneal dystrophy, and corneal transplantation.

Main Results:

• AI models demonstrate high accuracy in detecting corneal conditions, often matching or surpassing junior ophthalmologists.
• AI applications extend to automated image labeling and educational support.
• AI facilitates patient home monitoring for corneal health.

Conclusions:

• AI holds significant potential for enhancing early detection and standardized classification of corneal diseases.
• AI can improve clinical practice and support patient self-care in ophthalmology.
• AI integration promises advancements in managing corneal pathologies and preventing blindness.