Evaluating the Performance of Various Machine Learning Algorithms to Detect Subclinical Keratoconus

Abstract

Purpose: Keratoconus (KC) represents one of the leading causes of corneal transplantation worldwide. Detecting subclinical KC would lead to better management to avoid the need for corneal grafts, but the condition is clinically challenging to diagnose. We wished to compare eight commonly used machine learning algorithms using a range of parameter combinations by applying them to our KC dataset and build models to better differentiate subclinical KC from non-KC eyes. Methods: Oculus Pentacam was used to obtain corneal parameters on 49 subclinical KC and 39 control eyes, along with clinical and demographic parameters. Eight machine learning methods were applied to build models to differentiate subclinical KC from control eyes. Dominant algorithms were trained with all combinations of the considered parameters to select important parameter combinations. The performance of each model was evaluated and compared. Results: Using a total of eleven parameters, random forest, support vector machine and k-nearest neighbors had better performance in detecting subclinical KC. The highest area under the curve of 0.97 for detecting subclinical KC was achieved using five parameters by the random forest method. The highest sensitivity (0.94) and specificity (0.90) were obtained by the support vector machine and the k-nearest neighbor model, respectively. Conclusions: This study showed machine learning algorithms can be applied to identify subclinical KC using a minimal parameter set that are routinely collected during clinical eye examination. Translational Relevance: Machine learning algorithms can be built using routinely collected clinical parameters that will assist in the objective detection of subclinical KC.

Meibography Phenotyping and Classification From Unsupervised Discriminative Feature Learning

“…This image analysis technology could also be applied to other ophthalmic conditions, such as keratoconus (KC) and glaucoma. 10 , 11 However, in the proposed work, other factors (e.g. age, gender, and race) are not considered for analyzing the MG atrophy.…”

“… 5 , 6 AI has shown huge progress in the field of medicine, including cancer diagnosis, lung segmentation, and tumor detection, 7 – 9 especially in the ophthalmic domain. For example, AI has been applied to build models to detect subclinical Keratoconus, 10 , 11 which is the leading cause of corneal transplantation. Different AI systems were developed to detect the cases of glaucoma and have achieved promising performance.…”

A Deep Learning Model for Screening Multiple Abnormal Findings in Ophthalmic Ultrasonography (With Video)

“…The criteria for evaluating the performance of each model were optimized from 0 to 1 with values greater than 0.90, ranging between 0.8 and 0.9, between 0.5 and 0.79, and less than 0.5 defined as the highest, good, moderate, and poor performance in the present study. 23 A χ 2 test was performed to analyze the difference in accuracy between the model and experts. The Mann-Whitney U test was applied to compare the accuracy of the trainees with or without model assistant.…”

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“…In [ 21 ], authors have developed a classification system for keratoconus with an accuracy of 90% on a dataset of 40 images and 12 parameters. The authors of [ 22 ] have proposed eight classifiers in order to compare their performance. Using 11 extracted parameters on a dataset of 88 elements, RF, SVM, KNN, logistic regression (LR), linear discriminant analysis (LDA), lasso regression (LaR), DT, and multilayer perceptron neural network (MPAN) models provided an accuracy of 87%, 86%, 73%, 81%, 81%, 84%, 80%, and 52%, respectively.…”