TL;DR
This review provides guidance for building robust, interpretable, and deployable retinal visual computing systems in fusion-centric systems and foundation-model-centric systems.
Artificial intelligence (AI)-based vessel segmentation converts color fundus photographs, scanning laser ophthalmoscopy (SLO), optical coherence tomography angiography (OCTA), structural OCT, and anterior-segment images into quantitative vascular biomarkers. Strong performance within individual datasets has not consist...
Optical coherence tomography (OCT) provides cross-sectional views of retinal structures and is widely used to assess conditions that affect the macula. Automated analysis may help clinicians review large volumes of images, but a model’s performance can be affected by image quality, differences between datasets, class i...
The diagnosis of ophthalmic diseases such as glaucoma, diabetic retinopathy, hypertension, and cardiovascular disorders can be recognized through retinal vessel segmentation from fundus images. The low contrast, noise, blurred boundaries, and substantial variations in vessel thickness make the vessel segmentation a cha...
A systematic comparative evaluation of three deep learning-based segmentation architectures — U-Net, U-Net++, and Y-Net — for automated identification of DME and Intraretinal Fluid regions in OCT scans demonstrates the feasibility of deep learning-based OCT segmentation as a diagnostic support tool in resource-constrai...
Retinal vessel segmentation a critical diagnostic procedure for the early screening of ophthalmic and systemic diseases is undergoing a profound transformation driven by the integration of advanced deep learning and adaptive attention mechanisms. Conventional segmentation approaches, constrained by localized receptive...
This systematic review synthesizes the state-of-the-art computational pipelines, training paradigms, and explainability frameworks that enable the extraction of predictive systemic biomarkers from retinal imaging and highlights a methodological mandate for eXplainable Artificial Intelligence (XAI) in clinical translati...
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