Detection of subclinical atherosclerosis by image-based deep learning on chest X-ray.

ABSTRACT:

Aims

To develop a deep-learning-based system for recognition of subclinical atherosclerosis on a plain frontal chest X-ray.

Methods and results

A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was developed on 460 chest X-ray (80% training cohort, 20% internal validation cohort) of primary prevention patients [58.4% male, median age 63 (51-74) years] with available paired chest X-ray and chest computed tomography (CT) indicated for any clinical reason and performed within 3 months. The CAC score calculated on chest CT was used as ground truth. The model was validated on a temporally independent validation cohort of 90 patients from the same institution (external validation). The diagnostic accuracy of the AI-CAC model assessed by th

SUBMITTER: Gallone G 

PROVIDER: S-EPMC12282367 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature