# Artificial-intelligence-assisted CCTA quantifies sex differences in coronary atherosclerotic burden at low atheroma volumes

## TL;DR

AI-enhanced CCTA reveals that males have more non-calcified coronary plaque than females in low-risk groups, challenging assumptions about early heart disease risk.

## Contribution

AI-based CCTA is shown to detect sex-specific differences in coronary plaque composition at low atheroma volumes, which traditional methods may miss.

## Key findings

- Males had significantly higher total and non-calcified plaque volumes than females.
- Calcified and low-density plaque volumes were comparable between sexes.
- AI-CCTA can detect subclinical sex-based differences in coronary disease.

## Abstract

This visual summary highlights the use of artificial intelligence (AI) to analyze coronary computed tomography angiography (CCTA) in a low-risk cohort (TAV < 250 mm3). A male and female figure represent the study population, with AI-enhanced imaging illustrating the differential plaque composition. Males demonstrated significantly greater total and non-calcified atheroma volume than females, while calcified and low-density plaque volumes were comparable. This underscores the potential of AI-CCTA to uncover subclinical sex-based differences.

This visual summary highlights the use of artificial intelligence (AI) to analyze coronary computed tomography angiography (CCTA) in a low-risk cohort (TAV < 250 mm3). A male and female figure represent the study population, with AI-enhanced imaging illustrating the differential plaque composition. Males demonstrated significantly greater total and non-calcified atheroma volume than females, while calcified and low-density plaque volumes were comparable. This underscores the potential of AI-CCTA to uncover subclinical sex-based differences.

•AI-based CCTA was used to analyze coronary plaque in a low-risk cohort (TAV < 250 mm3).•Males had higher total and non-calcified plaque volumes than females.•No significant differences were found in calcified or low-density plaque by sex.•These findings challenge traditional assumptions about early CAD risk in females.•AI-driven plaque characterization enhances detection of early subclinical atherosclerosis.

AI-based CCTA was used to analyze coronary plaque in a low-risk cohort (TAV < 250 mm3).

Males had higher total and non-calcified plaque volumes than females.

No significant differences were found in calcified or low-density plaque by sex.

These findings challenge traditional assumptions about early CAD risk in females.

AI-driven plaque characterization enhances detection of early subclinical atherosclerosis.

Coronary artery disease (CAD) manifests differently between sexes, with data suggesting females develop more non-calcified plaques that traditional calcium-centric tools may not detect.

We conducted a retrospective cohort study of 100 individuals with low total atheroma volume (TAV) < 250 mm3 using artificial intelligence (AI)-enabled coronary computed tomography angiography (CCTA) to assess sex-based differences in coronary plaque composition. Plaque subtypes included calcified, non-calcified, and low-density non-calcified atheroma volumes.

Females had significantly lower total (p = 0.018) and non-calcified plaque (p < 0.001) burden compared to males. Calcified (p = 0.52) and low-density non-calcified (p = 0.16) plaque volumes did not differ significantly. Age was a consistent predictor of plaque volume across most subtypes.

Despite low overall plaque burden, males demonstrated a higher non-calcified plaque burden than females. This finding contrasts with previous literature and underscores the potential of AI-enabled CCTA to detect subclinical coronary disease, particularly in low-risk cohorts. These results support the use of comprehensive plaque profiling in both sexes to improve early risk stratification.