This review addresses the complexities of type 1 diabetes (T1D) and its associated complications, with a particular focus on diabetic retinopathy (DR). This review outlines the progression from non-proliferative to proliferative diabetic retinopathy and diabetic macular edema, highlighting the role of dysglycemia in the pathogenesis of these conditions. A significant portion of this review is devoted to technological advances in diabetes management, particularly the use of hybrid closed-loop systems (HCLSs) and to the potential of open-source HCLSs, which could be easily adapted to different patients' needs using big data analytics and machine learning. Personalized HCLS algorithms that integrate factors such as patient lifestyle, dietary habits, and hormonal variations are highlighted as critical to reducing the incidence of diabetes-related complications and improving patient outcomes.
Keywords: AndroidAps; DR; advanced hybrid closed-loop (AHCL) systems; continuous glucose monitoring (CGM); diabetes (T1D); diabetes management; epidemiology; machine learning; microangiopathic complications; neovascularization; non-proliferative DR (NPDR); open-source HCLSs; personalized medicine; proliferative DR (PDR).