Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores.

ABSTRACT:

Aims

This study aims to evaluate the ability of a deep-learning-based cardiovascular disease (CVD) retinal biomarker, Reti-CVD, to identify individuals with intermediate- and high-risk for CVD.

Methods and results

We defined the intermediate- and high-risk groups according to Pooled Cohort Equation (PCE), QRISK3, and modified Framingham Risk Score (FRS). Reti-CVD's prediction was compared to the number of individuals identified as intermediate- and high-risk according to standard CVD risk assessment tools, and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the results. In the UK Biobank, among 48 260 participants, 20 643 (42.8%) and 7192 (14.9%) were classified into the intermediate- and high-risk grou

SUBMITTER: Yi JK 

PROVIDER: S-EPMC10232236 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature