Corneal Topography Raw Data Classification Using a Convolutional Neural Network

American Journal of Ophthalmology
Pierre ZéboulonDamien Gatinel

Abstract

We investigated the efficiency of a convolutional neural network applied to corneal topography raw data to classify examinations of 3 categories: normal, keratoconus (KC), and history of refractive surgery (RS). Retrospective machine-learning experimental study. A total of 3,000 Orbscan examinations (1,000 of each class) of different patients of our institution were selected for model training and validation. One hundred examinations of each class were randomly assigned to the test set. For each examination, the raw numerical data from "elevation against the anterior best fit sphere (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 were calculated for the validation set. Overall classification accuracy of the validation set (n = 300) was 99.3% (98.3%-100%). Sensitivity and specificity were, respectively, 100% and 100% for KC, 100% and 99% (94.9%-100%) for normal examinations, and 98% (97.4%-100%) and 100% for RS...Continue Reading

Citations

Mar 27, 2021·Scientific Reports·François-Xavier CrahayDamien Gatinel
Aug 3, 2021·BMJ Open Ophthalmology·Xu ChenYalin Zheng
Aug 8, 2021·Journal of Clinical Medicine·Marta Jiménez-GarcíaUNKNOWN The REDCAKE Study Group
Dec 17, 2021·Translational Vision Science & Technology·Ali H Al-TimemySiamak Yousefi

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