AI-Powered Early Detection of Retinal Conditions: A Deep Learning Approach for Diabetic Retinopathy and Beyond

TL;DR

This paper presents a deep learning model that accurately detects retinal conditions like diabetic macular edema using OCT images, showing strong potential for clinical use.

Contribution

The study introduces a robust AI model for early detection of retinal diseases with high accuracy and performance metrics.

Findings

The model achieved 94.2% accuracy in detecting retinal conditions from OCT images.

Precision, recall, and F1 scores exceeded 92% across all disease classes.

Statistical analysis confirmed the model's robustness across validation folds.

Abstract

Various retinal conditions, such as diabetic macular edema (DME) and choroidal neovascularization (CNV), pose significant risks of visual impairment and vision loss. Early detection through automated and accurate and advanced systems can greatly enhance clinical outcomes for patients as well as for medical staff. This study is aimed at developing a deep learning–based model for the early detection of retinal diseases using OCT images. We utilized a publicly available retinal image dataset comprising images with DME, CNV, drusen, and normal cases. The Inception model was trained and validated using various evaluation metrics. Performance metrics, including accuracy, precision, recall, and F1 score, were calculated. The proposed model achieved an accuracy of 94.2%, with precision, recall, and F1 scores exceeding 92% across all classes. Statistical analysis demonstrated the robustness of the model across folds. Our findings highlight the potential of AI-powered systems in improving early detection of retinal conditions, paving the way for integration into clinical workflows. More efforts are needed to utilize it offline by making it available on ophthalmologist mobile devices to facilitate the diagnosis process and provide better service to patients.

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