The eyes possess sophisticated physiological structures, diverse disease targets, limited
drug delivery space, distinctive barriers, and complicated biomechanical processes …

The management of retinal diseases relies heavily on digital imaging data, including optical
coherence tomography (OCT) and fluorescein angiography (FA). Targeted feature extraction …

Purpose To evaluate the diagnostic accuracy of machine learning (ML) techniques applied
to radiomic features extracted from OCT and OCT angiography (OCTA) images for diabetes …

Diabetic retinopathy (DR) is a microvascular complication of diabetes and the most common
cause of acquired vision loss in adults worldwide. DR is associated with long-term chronic …

Background Radiomics holds great potential in medical image analysis for various
ophthalmic diseases. In recent times, there have been numerous endeavors in this area of …

Endothelial dysfunction and microvascular disease have been shown to play an important
role in the development and progression of heart failure (HF). Retinal imaging provides a …

Objective: Diabetic macular edema (DME) and retinal vein occlusion (RVO) are the leading
causes of visual impairments across the world. Vascular endothelial growth factor (VEGF) …

Geographic atrophy (GA) is an advanced form of dry age-related macular degeneration
(AMD) that leads to progressive and irreversible vision loss. Identifying patients with greatest …

Purpose No established biomarkers currently exist for therapeutic efficacy and durability of
anti–VEGF therapy in neovascular age-related macular degeneration (nAMD). This study …

Purpose: Machine learning models based on radiomic feature extraction from clinical
imaging data provide effective and interpretable means for clinical decision making. This …