Prediction of coronary artery disease based on facial temperature information captured by non-contact infrared thermography.

ABSTRACT:

Background

Current approaches for initial coronary artery disease (CAD) assessment rely on pretest probability (PTP) based on risk factors and presentations, with limited performance. Infrared thermography (IRT), a non-contact technology that detects surface temperature, has shown potential in assessing atherosclerosis-related conditions, particularly when measured from body regions such as faces. We aim to assess the feasibility of using facial IRT temperature information with machine learning for the prediction of CAD.

Methods

Individuals referred for invasive coronary angiography or coronary CT angiography (CCTA) were enrolled. Facial IRT images captured before confirmatory CAD examinations were used to develop and validate a deep-learning IRT image model for detecting CA

SUBMITTER: Kung M 

PROVIDER: S-EPMC11149132 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature