A Deep Learning Approach for Predicting the Response to Anti-VEGF Treatment in Diabetic Macular Edema Patients Using Optical Coherence Tomography Images
Résumé
Diabetic macular edema (DME) is a serious complication of diabetes that can lead to vision loss, making the prediction of patient response to anti-vascular endothelial growth factor (anti-VEGF) treatment crucial for optimizing therapeutic strategies. This study introduces ESSDP (Extended Siam Saves Diabetes Patients), a novel deep learning approach leveraging a Siamese network architecture with EfficientNetB2 to predict therapeutic response in DME patients through optical coherence tomography (OCT) image analysis. By classifying patients into good or poor responder groups based on central macular thickness reduction after injection, the proposed framework achieved a predictive performance with an accuracy of 0.80, sensitivity of 0.71, precision of 0.89, and an F1-Score of 0.74. These findings highlight the potential of Siamese network-based deep learning architectures as effective tools for predicting treatment outcomes in DME patients, even when working with limited datasets, and pave the way for enhancing personalized treatment strategies in ophthalmology.