Application of deep learning algorithm for judicious use of anti-VEGF in diabetic macular edema.

Diabetic Macular Edema (DME) is a major complication of diabetic retinopathy characterized by fluid accumulation in the macula, leading to vision impairment. The standard treatment involves anti-VEGF (Vascular Endothelial Growth Factor) therapy, but approximately 36% of patients do not respond adequately, highlighting the need for more precise predictive models to guide treatment. This study aims to develop a Hybrid Deep Learning model to predict treatment responses in DME patients undergoing anti-VEGF therapy, thereby improving the accuracy of treatment planning and minimizing the unnecessary use of costly anti-VEGF agents. The model integrates both Optical Coherence Tomography (OCT) images and clinical data from 181 patients, including key parameters such as serum VEGFR-2 concentration and the duration of DME. The architecture combines convolutional neural networks (CNNs) for image data with multi-layer perceptron (MLP) for tabular clinical data, allowing for a comprehensive analysis of both data types. These pathways converge into a unified predictive framework designed to enhance the model's accuracy. This study utilized a Hybrid Deep Learning model that achieved an 85% accuracy, with additional metrics including precision, recall, and the area under the receiver operating characteristic curve (AUC-ROC) confirming its robustness and reliability. The findings suggest that the model accurately predicts patient responses to anti-VEGF therapy, paving the way for more personalized and targeted treatment strategies. This approach has the potential to enhance patient outcomes and minimize unnecessary administration of anti-VEGF agents, thereby optimizing therapeutic interventions in ophthalmology.