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.

Keratoconus is characterised by reduced rigidity of the cornea with distortion and focal thinning that causes blurred vision, however, the pathogenetic mechanisms are unknown. It can lead to severe visual morbidity in children and young adults and is a common indication for corneal transplantation worldwide. Here we report the first large scale genome-wide association study of keratoconus including 4,669 cases and 116,547 controls. We have identified significant association with 36 genomic loci that, for the first time, implicate both dysregulation of corneal collagen matrix integrity and cell differentiation pathways as primary disease-causing mechanisms. The results also suggest pleiotropy, with some disease mechanisms shared with other corneal diseases, such as Fuchs endothelial corneal dystrophy. The common variants associated with keratoconus explain 12.5% of the genetic variance, which shows potential for the future development of a diagnostic test to detect susceptibility to disease.