Diabetic retinopathy screening with confocal fundus camera and artificial intelligence - assisted grading.

Purpose: Screening for diabetic retinopathy (DR) by ophthalmologists is costly and labour-intensive. Artificial Intelligence (AI) for automated DR detection could be a clinically and economically alternative. We assessed the performance of a confocal fundus imaging system (DRSplus, Centervue SpA), coupled with an AI algorithm (RetCAD, Thirona B.

Hospitalisation for herpes zoster in people with and without diabetes: A 10-year-observational study.

Aims: This study explores the association between Herpes Zoster (HZ) hospitalizations and diabetes in Piedmont, Italy from 2010 to 2019. Focusing on the burden of HZ hospitalizations in diabetic and non-diabetic groups, it aims to identify risk factors in diabetics to enhance prevention strategies.

Methods: In a two-phase study, we first compared age-standardized HZ hospitalization rates between diabetic and non-diabetic individuals from 2010 to 2019.

Feasibility and accuracy of the screening for diabetic retinopathy using a fundus camera and an artificial intelligence pre-evaluation application.

Aims: Periodical screening for diabetic retinopathy (DR) by an ophthalmologist is expensive and demanding. Automated DR image evaluation with Artificial Intelligence tools may represent a clinical and cost-effective alternative for the detection of retinopathy. We aimed to evaluate the accuracy and reliability of a machine learning algorithm.

Hepatocellular carcinoma in a large cohort of type 2 diabetes patients.

Aims: To elucidate the current burden of hepatocellular carcinoma (HCC) in type 2 diabetes (DM2) with a focus on the associated clinical determinants.

Methods: Incidence of HCC between 2009 and 2019 in the diabetic and general population was calculated from regional administrative and hospital databases. Potential determinants of the disease were evaluated with a follow-up study.