Deep Learning Algorithm for Keratoconus Detection from Tomographic Maps and Corneal Biomechanics: A Diagnostic Study

Abstract

Purpose To develop an artificial intelligence (AI) approach for differentiating between normal cornea, subclinical, and keratoconus (KC) using tomographic maps from Pentacam (Oculus) and corneal biomechanics from Corvis ST (Oculus). Methods A total of 1,668 tomographic (769 patients) and 611 biomechanical (307 patients) images from the Chula Refractive Surgery Center, King Chulalongkorn Memorial Hospital were included. The sample size was divided into the Pentacam and combined Pentacam-Corvis groups. Different convolutional neural network approaches were used to enhance the KC and subclinical KC detection performance. Results AI model 1, which obtained refractive maps from Pentacam, achieved an area under the receiver operating characteristic curve (AUC) of 0.938 and accuracy of 0.947 (sensitivity, 90.8% and specificity, 96.9%). AI model 2, which added dynamic corneal response and the Vinciguerra screening report from Corvis ST to AI Model 1, achieved an AUC of 0.985 and accuracy of 0.956 (sensitivity, 93.0% and specificity, 94.3%). AI model 3, which added the corneal biomechanical index to AI Model 2, reached an AUC of 0.991 and accuracy of 0.956 (sensitivity, 93.0% and specificity, 94.3%). Conclusions Our study showed that AI models using either anterior corneal curvature alone or combined with corneal biomechanics could help classify normal and keratoconic corneas, which would make diagnosis more accurate and would be helpful in decision-making for the treatment.

Keywords

• artificial intelligence
• forme fruste keratoconus
• keratoconus
• keratoconus suspect
• machine learning
• subclinical keratoconus