Advances in Corneal Diagnostics Using Machine Learning.

This paper provides comprehensive insights into the cornea and its diseases, with a particular focus on keratoconus. This paper explores the cornea's function in maintaining ocular health, detailing its anatomy, pathological conditions, and the latest developments in diagnostic techniques. Keratoconus is discussed extensively, covering its subtypes, etiology, clinical manifestations, and the application of the Q-value for quantification. Several diagnostic techniques, such as corneal topography, are crucial points of discussion. This paper also examines the use of machine learning models, specifically Decision Tree and Nearest Neighbor Analysis, which enhance the accuracy of diagnosing based on topographical corneal parameters from corneal topography. These models provide valuable insights into disease progression and aid in clinical decision making. Integrating these technologies in medical research opens promising avenues for enhanced disease detection. Our findings demonstrate the effectiveness of Decision Tree and Nearest Neighbor Analysis in classifying and predicting conditions based on corneal parameters. The Decision Tree achieved classification accuracy of 62% for training and 65.7% for testing, while Nearest Neighbor Analysis yielded 65.4% for training and 62.6% for holdout samples. These models offer valuable insights into the progression and severity of keratoconus, aiding clinicians in treatment and management decisions.