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Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...
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Artificial intelligence in corneal diseases: A narrative review.

Tuan Nguyen1, Joshua Ong2, Mouayad Masalkhi3

  • 1Weill Cornell/Rockefeller/Sloan-Kettering Tri-Institutional MD-PhD Program, New York City, NY, United States.

Contact Lens & Anterior Eye : the Journal of the British Contact Lens Association
|August 28, 2024
PubMed
Summary

Artificial intelligence (AI) aids in diagnosing and managing corneal diseases like keratoconus and infectious keratitis. AI models show high accuracy, improving eye care accessibility, especially in underserved regions.

Keywords:
Artificial intelligenceCorneaDeep learningMachine learning

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Area of Science:

  • Ophthalmology
  • Medical Artificial Intelligence
  • Biomedical Engineering

Background:

  • Corneal diseases pose a significant global health challenge, particularly in areas with limited access to specialized eye care.
  • Artificial intelligence (AI) presents a transformative approach to automate the diagnosis and management of various corneal conditions.

Purpose of the Study:

  • To conduct a narrative review on the application of AI in diagnosing and managing key corneal diseases.
  • To explore emerging trends and challenges in AI for corneal disease management.

Main Methods:

  • Review of AI applications across diverse corneal conditions including keratoconus, infectious keratitis, pterygium, dry eye disease, Fuchs endothelial corneal dystrophy, and corneal transplantation.
  • Analysis of AI models integrating multimodal imaging (corneal topography, slit-lamp, OCT) and clinical data.

Main Results:

  • AI models demonstrate high diagnostic accuracy, frequently surpassing human expert performance.
  • Emerging AI trends include using biomechanical data for keratoconus detection and in vivo confocal microscopy for infectious keratitis diagnosis.
  • AI shows promise in predicting disease progression, treatment success, and post-transplant complications.

Conclusions:

  • AI offers a powerful tool for enhancing the diagnosis and management of corneal diseases, improving accessibility to eye care.
  • Addressing challenges like data heterogeneity, validation, and model interpretability is crucial for widespread AI adoption in ophthalmology.