Advances in Corneal Diagnostics Using Machine Learning

Area of Science:

• Ophthalmology
• Medical Diagnostics
• Computational Biology

Background:

• The cornea is vital for vision, and its diseases, like keratoconus, significantly impact ocular health.
• Accurate diagnosis of corneal conditions, particularly keratoconus, is essential for timely intervention and management.
• Corneal topography is a key diagnostic tool, providing topographical corneal parameters.

Purpose of the Study:

• To explore the anatomy and pathology of the cornea, focusing on keratoconus.
• To investigate the utility of machine learning models for diagnosing keratoconus based on corneal topography.
• To evaluate the effectiveness of Decision Tree and Nearest Neighbor Analysis in classifying and predicting keratoconus.

Main Methods:

• Review of corneal anatomy, pathology, and diagnostic techniques.
• Application of Decision Tree and Nearest Neighbor Analysis to topographical corneal parameters.
• Analysis of classification accuracy for training, testing, and holdout samples.

Main Results:

• Decision Tree achieved 62% training and 65.7% testing accuracy.
• Nearest Neighbor Analysis achieved 65.4% training and 62.6% holdout accuracy.
• Both models demonstrated effectiveness in classifying and predicting conditions based on corneal parameters.

Conclusions:

• Machine learning models, specifically Decision Tree and Nearest Neighbor Analysis, enhance the accuracy of keratoconus diagnosis.
• These models provide valuable insights into disease progression and severity.
• Integration of these technologies aids clinicians in treatment and management decisions for keratoconus.