AI in Ophthalmic Imaging: Enhancing Retina Analysis for Early Disease Detection

In this review, we analyze the AI-based methods for retina segmentation and classification in ophthalmology/medical imaging with depth. This study comprises the whole research pipeline, including data retrieval and preprocessing, feature extraction classification as well as segmentation. It explores a mixture of machine and deep learning algorithms for these tasks, monitoring their performance using relevant metrics. Illustrating the stakes using early-onset disease detection in retinal image analysis, this review describes manual annotation limitation and provides outlines for AI solutions. It provides information on datasets used in previous works, describing their features and preprocessing procedures. This review details feature extraction from traditional image processing to deep learning, focusing on characteristic features for differentiating retinal abnormalities. The classification part describes several models used to classify the retinal images and its evaluation in terms of Accuracy, Precision, Recall and F1-score. The proposed work also shows approaches to segment the region of interest in retina images and scoring mechanism such as Dice Coefficient, Intersection Over Union for evaluating segmentation. The article closes by reviewing summary points, stating limitations and directions for future research. To sum up, this all-in-one article is beneficial for academia and industry professionals not only to reveal the state-of-the-art techniques but also provide a guideline toward future research in AI-based solutions with improved diagnosis & treatment of retinal diseases.