Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning.

ABSTRACT: Cardiovascular diseases (CVD) are the leading cause of death worldwide. People affected by CVDs may go undiagnosed until the occurrence of a serious heart failure event such as stroke, heart attack, and myocardial infraction. In Qatar, there is a lack of studies focusing on CVD diagnosis based on non-invasive methods such as retinal image or dual-energy X-ray absorptiometry (DXA). In this study, we aimed at diagnosing CVD using a novel approach integrating information from retinal images and DXA data. We considered an adult Qatari cohort of 500 participants from Qatar Biobank (QBB) with an equal number of participants from the CVD and the control groups. We designed a case-control study with a novel multi-modal (combining data from multiple modalities-DXA and retinal images)-to propose a d

SUBMITTER: Al-Absi HRH 

PROVIDER: S-EPMC9228833 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature