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

Abstract

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.

Figure 1.

Heatmap of concentrations of 28 immune mediators in 522 vitreous samples collected from eyes diagnosed with 12 intraocular diseases. Data were log transformed, and all negative values after log transformation were set to 0. The averaged values for each disease are color coded in a blue-white-red (low to high) scale. Clustering was conducted using Pearson correlation. Prominent clusters are labeled (1) to (4).

Figure 2.

Boxplot comparing the accuracy of five machine learning algorithms using vitreous concentrations of 28 immune mediators to predict 12 intraocular diseases. Each F-score was obtained from 100 independent iterations of stratified fivefold cross-validation. The accuracy is significantly higher in RF compared with the other four algorithms. NB, naïve Bayes classifier; RBF, radial basis function. ** P < 0.001 (Mann–Whitney U test).

Figure 3.

Prediction of 12 intraocular diseases using RF based on 28 vitreous immune mediators. Data were obtained from 100 independent iterations of stratified fivefold cross-validation. Finally, the scores for each fold were summed and averaged. (A) Average confusion matrix constructed to examine whether the predicted classes match the actual classes in the intraocular disease cohort. The rows of the matrix represent the actual classes, and the columns represent the predicted classes. Each cell shows the number of cases where the actual class (row) is predicted as the class in the column. (B) Diagram showing recall results from the first to the third prediction in predicting 12 intraocular diseases by random forest modeling. Cumulative percentage of prediction is given next to each predicted disease.

Figure 4.

F-scores using random forest based on 28 vitreous immune mediators to predict 12 intraocular diseases. Each F-score was obtained from 100 independent iterations of stratified fivefold cross-validation. (A) Boxplot of F-scores for the 12 intraocular diseases. (B) Chart of overall accuracy for 12 classes and F-scores for 12 individual diseases.

Figure 5.

Prediction of 12 intraocular diseases using random forest based on 28 vitreous immune mediators combined with 26 blood test data. Data were obtained from 100 independent iterations of stratified fivefold cross-validation. Finally, the scores for each fold were summed and averaged. (A) Boxplot of F-scores for the 12 intraocular diseases. (B) Chart of overall accuracy for 12 classes, and F-scores for 12 individual diseases.

Figure 6.

Prediction of 12 intraocular diseases using random forest based on 26 blood test data only. Data were obtained from 100 independent iterations of stratified fivefold cross-validation. Finally, the scores for each fold were summed and averaged. (A) Boxplot of F-scores for the 12 intraocular diseases. (B) Chart of overall accuracy for 12 classes, and F-scores for 12 individual diseases.

Figure 7.

Performance of random forest to predict 12 intraocular diseases and discriminate each disease from the others, based on 28 immune mediators in vitreous. Data were obtained from 100 independent iterations of stratified fivefold cross-validation. (A) Line graphs showing average ROC curves for 12 intraocular diseases. (B) Line graphs showing average PR curves for 12 intraocular diseases. (C) Chart of AUC-ROC and AUC-PR for 12 intraocular diseases.

Figure 8.

Boxplots showing the relative importance of 28 immune mediators for discriminating each disease from the others using random forest. The boxplots for the top five intraocular diseases with the highest F-score are shown: (A) vitreoretinal lymphoma, (B) endophthalmitis, (C) uveal melanoma, (D) rhegmatogenous retinal detachment, and (E) acute retinal necrosis. The importance of each immune mediator was computed as the feature importance. The data were obtained after 100 independent iterations of stratified fivefold cross-validation.

Figure 9.

Boxplots illustrating the most important features for the top five intraocular diseases with the highest F-score: (A) IL-10 for vitreoretinal lymphoma, (B) IL-6 for endophthalmitis, (C) RANTES for uveal melanoma, (D) MCP-1 for rhegmatogenous retinal detachment, and (E) IFN-γ for acute retinal necrosis. *P < 0.05, **P < 0.001 (Mann–Whitney U test).