CLAiR Pivotal Study

Summary

This is a multi-site, observational clinical study to validate the performance of the CLAiR AI software in identifying elevated atherosclerotic cardiovascular disease (ASCVD) risk from retinal (eye) images obtained from two different retinal image camera models.

Detailed Description

This is a prospective observational clinical study to collect retinal images and clinical biomarker data in order to analyze the performance of the CLAiR SaMD compared to the reference PCE risk score. CLAiR is a deep learning (DL) model that uses retinal photographs and limited demographic data to classify an individual's risk of developing ASCVD over the next 10 years as elevated (≥7.5%) or non-elevated (\<7.5%). For validation, the output of the algorithm can be directly compared to the PCE output, a widely accepted algorithm used by Healthcare Providers to predict ASCVD risk in patients. The primary hypothesis is that the CLAiR SaMD can achieve high sensitivity and specificity in the binary determination of Yes/No elevated ASCVD risk with PCE risk score ≥7.5% as the reference standard.

Eligibility Criteria

Interventions