Harnessing deep learning for detection of diabetic retinopathy in geriatric group using optical coherence tomography angiography-OCTA: A promising approach

Abstract

The prevalence of diabetic retinopathy (DR) among the geriatric population poses significant challenges for early detection and management. Optical Coherence Tomography Angiography (OCTA) combined with Deep Learning presents a promising avenue for improving diagnostic accuracy in this vulnerable demographic. In this method, we propose an innovative approach utilizing OCTA images and Deep Learning algorithms to detect diabetic retinopathy in geriatric patients. We have collected 262 OCTA scans of 179 elderly individuals, both with and without diabetes, and trained a deep-learning model to classify retinopathy severity levels. Convolutional Neural Network (CNN) models: Inception V3, ResNet-50, ResNet50V2, VggNet-16, VggNet-19, DenseNet121, DenseNet201, EfficientNetV2B0, are trained to extract features and further classify them.

Here we demonstrate:

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  The potential of OCTA and Deep Learning in enhancing geriatric eye care at the very initial stage.

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  The importance of technological advancements in addressing age-related ocular diseases and providing reliable assistance to clinicians for DR classification.

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  The efficacy of this approach in accurately identifying diabetic retinopathy stages, thereby facilitating timely interventions, and preventing vision loss in the elderly population.