Machine Learning Approach for Intraocular Disease Prediction Based on Aqueous Humor Immune Mediator Profiles
Authors: Naoya Nezu, Yoshihiko Usui, Akira Saito, Hiroyuki Shimizu, Masaki Asakage, Naoyuki Yamakawa, Kinya Tsubota, Yoshihiro Wakabayashi, Akitomo Narimatsu, Kazuhiko Umazume, Katsuhiko Maruyama, Masahiro Sugimoto, Masahiko Kuroda, Hiroshi Goto
Journal: Ophthalmology (2021)

PurposeVarious immune mediators have crucial roles in the pathogenesis of intraocular diseases. Machine learning can be used to automatically select and weigh various predictors to develop models maximizing predictive power. However, these techniques have not yet been applied extensively in studies focused on intraocular diseases. We evaluated whether 5 machine learning algorithms applied to the data of immune-mediator levels in aqueous humor can predict the actual diagnoses of 17 selected intraocular diseases and identified which immune mediators drive the predictive power of a machine learning model.DesignCross-sectional study.ParticipantsFive hundred twelve eyes with diagnoses from among 17 intraocular diseases.MethodsAqueous humor samples were collected, and the concentrations of 28 immune mediators were determined using a cytometric bead array. Each immune mediator was ranked according to its importance using 5 machine learning algorithms. Stratified k-fold cross-validation was used in evaluation of algorithms with the dataset divided into training and test datasets.Main Outcome MeasuresThe algorithms were evaluated in terms of precision, recall, accuracy, F-score, area under the receiver operating characteristic curve, area under the precision-recall curve, and mean decrease in Gini index.ResultsAmong the 5 machine learning models, random forest (RF) yielded the highest classification accuracy in multiclass differentiation of 17 intraocular diseases. The RF prediction models for vitreoretinal lymphoma, acute retinal necrosis, endophthalmitis, rhegmatogenous retinal detachment, and primary open-angle glaucoma achieved the highest classification accuracy, precision, and recall. Random forest recognized vitreoretinal lymphoma, acute retinal necrosis, endophthalmitis, rhegmatogenous retinal detachment, and primary open-angle glaucoma with the top 5 F-scores. The 3 highest-ranking relevant immune mediators were interleukin (IL)-10, interferon-γ-inducible protein (IP)-10, and angiogenin for prediction of vitreoretinal lymphoma; monokine induced by interferon γ, interferon γ, and IP-10 for acute retinal necrosis; and IL-6, granulocyte colony-stimulating factor, and IL-8 for endophthalmitis.ConclusionsRandom forest algorithms based on 28 immune mediators in aqueous humor successfully predicted the diagnosis of vitreoretinal lymphoma, acute retinal necrosis, and endophthalmitis. Overall, the findings of the present study contribute to increased knowledge on new biomarkers that potentially can facilitate diagnosis of intraocular diseases in the future. Various immune mediators have crucial roles in the pathogenesis of intraocular diseases. Machine learning can be used to automatically select and weigh various predictors to develop models maximizing predictive power. However, these techniques have not yet been applied extensively in studies focused on intraocular diseases. We evaluated whether 5 machine learning algorithms applied to the data of immune-mediator levels in aqueous humor can predict the actual diagnoses of 17 selected intraocular diseases and identified which immune mediators drive the predictive power of a machine learning model. Cross-sectional study. Five hundred twelve eyes with diagnoses from among 17 intraocular diseases. Aqueous humor samples were collected, and the concentrations of 28 immune mediators were determined using a cytometric bead array. Each immune mediator was ranked according to its importance using 5 machine learning algorithms. Stratified k-fold cross-validation was used in evaluation of algorithms with the dataset divided into training and test datasets. The algorithms were evaluated in terms of precision, recall, accuracy, F-score, area under the receiver operating characteristic curve, area under the precision-recall curve, and mean decrease in Gini index. Among the 5 machine learning models, random forest (RF) yielded the highest classification accuracy in multiclass differentiation of 17 intraocular diseases. The RF prediction models for vitreoretinal lymphoma, acute retinal necrosis, endophthalmitis, rhegmatogenous retinal detachment, and primary open-angle glaucoma achieved the highest classification accuracy, precision, and recall. Random forest recognized vitreoretinal lymphoma, acute retinal necrosis, endophthalmitis, rhegmatogenous retinal detachment, and primary open-angle glaucoma with the top 5 F-scores. The 3 highest-ranking relevant immune mediators were interleukin (IL)-10, interferon-γ-inducible protein (IP)-10, and angiogenin for prediction of vitreoretinal lymphoma; monokine induced by interferon γ, interferon γ, and IP-10 for acute retinal necrosis; and IL-6, granulocyte colony-stimulating factor, and IL-8 for endophthalmitis. Random forest algorithms based on 28 immune mediators in aqueous humor successfully predicted the diagnosis of vitreoretinal lymphoma, acute retinal necrosis, and endophthalmitis. Overall, the findings of the present study contribute to increased knowledge on new biomarkers that potentially can facilitate diagnosis of intraocular diseases in the future.