LAUSR

PURPOSE We investigated the efficiency of a convolutional neural network applied to corneal topography raw data to classify examinations of three categories: Normal , Keratoconus (KC) and History of refractive surgery (RS). DESIGN Retrospective machine learning experimental study. METHOD 3,000 Orbscan examinations (1000 of each class) of different patients of our institution were selected for model training and validation. 100 examinations of each class were randomly assigned to the test set. For each examination, the raw numerical data from 'Elevation against the anterior BFS', ´Elevation against the posterior BFS', 'Axial anterior curvature', and 'Pachymetry' maps were used. Each map was a square matrix of 2,500 values. The 4 maps were stacked and used as if they were 4 channels of a single image.A convolutional neural network was built and trained on the training set. Classification accuracy and class wise sensitivity and specificity was calculated for the validation set. RESULTS Overall classification accuracy of the validation set (n = 300) was 99.3% (98.3%-100%):. Sensitivity and specificity were respectively 100% and 100% for Keratoconus, 100% and 99% (94.9% - 100%) for Normal examinations, and 98% (97.4% - 100%) and 100% for RS examinations. CONCLUSION Using combined corneal topography raw data with a convolutional neural network is a very effective way to classify examinations and probably the most thorough way to automatically analyze corneal topography. It should be considered for other routine tasks performed on corneal topography, such as refractive surgery screening.