Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging.

Background: Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina's unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care.

Faster smooth muscle cell coverage in ultrathin-strut drug-eluting stent leads to earlier re-endothelialization.

The ultrathin-strut drug-eluting stent (DES) has shown better clinical results than thin- or thick-strut DES. We investigated if re-endothelialization was different among three types of DES: ultrathin-strut abluminal polymer-coated sirolimus-eluting stent (SES), thin-strut circumferential polymer-coated everolimus-eluting stent (EES), and thick-strut polymer-free biolimus-eluting stent (BES) to gain insight into the effect of stent design on promoting vascular healing. After implanting three types of DES in the coronary arteries of minipigs, we performed optical coherence tomography (OCT) at weeks 2, 4, and 12 ( = 4, each).