Towards Sustainable Retinal Diagnostics A Deep Learning Alternative to OCT for Macular Thickness Estimation

Diabetic Macular Edema (DME) is a major cause of vision impairment among diabetic patients, especially in areas lacking advanced diagnostic tools like Optical Coherence Tomography (OCT). This study proposes a cost-effective, AI-based method to estimate macular thickness—a key DME biomarker—using retinal fundus images and clinical data. A dataset of 176 colour fundus images with corresponding macular thickness and clinical information from DME patients at Amrita Institute of Medical Sciences, Kochi, India, was augmented to about 3,000 images to improve model performance. Manual macular region annotations were done using the CVAT tool, followed by segmentation using a U-Net-based deep learning model. For predicting macular thickness, three convolutional neural networks—InceptionV3, DenseNet121, and EfficientNetB0—were tested in a multimodal framework combining image features with clinical parameters like visual acuity and diabetes duration. EfficientNetB0 achieved the best performance, with a test Mean Squared Error (MSE) of 0.2518 and an R² score of 0.7752. This approach enables accurate, low-cost, and scalable diagnostics in underserved regions, aligning with Sustainable Development Goals: ensuring healthy lives (SDG 3), fostering innovation (SDG 9), and reducing inequalities (SDG 10). The results suggest that this AI-based method can serve as a viable alternative to OCT for macular thickness estimation, supporting early detection and monitoring of DME in low-resource settings.