Ensemble machine learning algorithm for anti-VEGF treatment efficacy prediction in diabetic macular edema

Diabetic macular edema (DME) is a leading cause of vision loss in diabetes, with variable responses to anti-vascular endothelial growth factor (anti-VEGF) therapy in DME patients. Current diagnosis relies on optical coherence tomography (OCT) imaging, but manual interpretation is limited. This study aims to integrate 3D-OCT features and clinical variables to develop machine learning (ML) models for predicting anti-VEGF treatment outcomes.

Medical records and 3D-OCT images of DME patients were included in this study. The 3D-OCT images were categorized into good and poor visual response groups based on the best corrected visual acuity at one month after three consecutive anti-VEGF treatments. The images and clinical features were subjected to assessment by 11 automatic classification models for anti-VEGF treatment responses in DME patients. The top 3 performing models were selected to build an ensemble model, and evaluated in the test dataset.

This study included 142 patients with 3D-OCT images of 170 eyes. A total of 20 image and clinical features were selected for the model construction and test in DME patients responded to anti-VEGF therapy. Adaptive boosting (AdaBoost), GradientBoosting, and light gradient boosting machine (LightGBM) exhibited better performances than the remaining 8 models. The ensemble model constructed achieved a sensitivity of 0.941, specificity of 0.882, and accuracy of 0.912 in the test dataset, with an area under the receiver operating characteristic curve of 0.976.

This study established an ensemble ML algorithm based on 3D-OCT images and clinical features for automatic detection of treatment responses to anti-VEGF treatment in DME patients to predict the efficacy of anti-VEGF treatment in DME patients and assist clinicians in optimal treatment decisions.