EMKLAS: A New Automatic Scoring System for Early and Mild Keratoconus Detection.

Purpose: Create a unique predictive model based on a set of demographic, optical, and geometric variables with two objectives: classifying keratoconus (KC) in its first clinical manifestation stages and establishing the probability of having correctly classified each case. Methods: We selected 178 eyes of 178 subjects (115 males; 64.6%; 63 females, 35.4%). Of these, 74 were healthy control subjects, and 104 suffered from KC according to the RETICS grading system (61 early KC, 43 mild KC). Only one eye from each patient was selected, and 27 different parameters were studied (demographic, clinical, pachymetric, and geometric). The data obtained were used in an ordinal logistic regression model programmed as a web application capable of using new patient data for real-time predictions. Results: EMKLAS, an early and mild KC classifier, showed good training performance figures, with 73% global accuracy and a 95% confidence interval of 65% to 79%. This classifier is particularly accurate when validated by an independent sample for the control (79%) and mild KC (80%) groups. The accuracy of the early KC group was remarkably lower (69%). The variables included in the model were age, gender, corrected distance visual acuity, 8-mm corneal diameter, and posterior minimum thickness point deviation. Conclusions: Our web application allows fast, objective, and quantitative assessment of early and mild KC in detection and classification terms and assists ophthalmology professionals in diagnosing this disease. Translational Relevance: No single gold standard exists for detecting and classifying preclinical KC, but the use of our web application and EMKLAS score may aid the decision-making process of doctors.