An Approach to Predict Intraocular Diseases by Machine Learning Based on Vitreous Humor Immune Mediator Profile.

Purpose: This study aimed to elucidate whether machine learning algorithms applied to vitreous levels of immune mediators predict the diagnosis of 12 representative intraocular diseases, and identify immune mediators driving the predictive power of machine learning model. Methods: Vitreous samples in 522 eyes diagnosed with 12 intraocular diseases were collected, and 28 immune mediators were measured using a cytometric bead array. The significance of each immune mediator was determined by employing five machine learning algorithms. Stratified k-fold cross-validation was performed to divide the dataset into training and test sets. The algorithms were assessed by analyzing precision, recall, accuracy, F-score, area under the receiver operating characteristics curve, area under the precision-recall curve, and feature importance. Results: Of the five machine learning models, random forest attained the maximum accuracy in the classification of 12 intraocular diseases in a multi-class setting. The random forest prediction models for vitreoretinal lymphoma, endophthalmitis, uveal melanoma, rhegmatogenous retinal detachment, and acute retinal necrosis demonstrated superior classification accuracy, precision, and recall. The top three important immune mediators for predicting vitreoretinal lymphoma were IL-10, granzyme A, and IL-6; those for endophthalmitis were IL-6, G-CSF, and IL-8; and those for uveal melanoma were RANTES, IL-8 and bFGF. Conclusions: The random forest algorithm effectively classified 28 immune mediators in the vitreous to accurately predict the diagnosis of vitreoretinal lymphoma, endophthalmitis, and uveal melanoma among 12 representative intraocular diseases. In summary, the results of this study enhance our understanding of potential new biomarkers that may contribute to elucidating the pathophysiology of intraocular diseases in the future.