Automated proximal coronary artery calcium identification using artificial intelligence: advancing cardiovascular risk assessment
Authors' conclusion
The AI algorithm could identify proximal CAC on CAC CT. The proximal location had modest prognostic significance in subjects with mild CAC scores. The AI identification of proximal CAC can be integrated into automatic CAC scoring and improves the risk prediction of CAC CT.
Study context
- Population
- Asymptomatic adults with baseline coronary artery calcium (CAC) CT scansasymptomatic adults with baseline CAC CT scans · subjects with expert annotations 303 · additional subjects with mild artificial intelligence (AI)-derived CAC scores (CAC score 1-99) 588
- Participants
- 2,016 adults
- Index test or model
- AI classification of proximal vs. non-proximal CAC against expert annotations
- Target condition
- proximal coronary artery calcium (CAC)
Methodological detailssingle site, 14-year follow-up
- Single site
- 14-year follow-up
Performance
As reported in the abstract
Area under receiver operating curve
0.93 [95% confidence interval (CI) 0.91-0.95]
Further findings
Risk of major adverse cardiac events (MACE) with AI proximal involvement (mild AI-derived CAC scores)
| HR for MACE (95% CI) | P value | ||
|---|---|---|---|
| AI proximal involvementAdjusting for CAC score alone | 2.28 (1.16–4.48) | P = 0.02 | |
| AI proximal involvementAdjusting for CAC score and clinical risk factors | 2.12 (1.03–4.36) | P = 0.04 | |
| 0.20.5125HR for MACE (log scale) |
MACE-free survival with AI proximal involvement (mild AI-derived CAC scores, CAC score 1-99)
- AI proximal involvement associated with worse MACE-free survival
- P = 0.008
- 588 subjects with mild AI-derived CAC scores
Original publication
Automated proximal coronary artery calcium identification using artificial intelligence: advancing cardiovascular risk assessment. Eur Heart J Cardiovasc Imaging. Published 16 January 2025. doi:10.
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