AI quantification of breast arterial calcifications (BACs) on mammograms enhances prediction of cardiovascular events beyond traditional risk models.
Key Details
- 1AI model (cmAngio, CureMetrix) used on 21,514 women aged 40+ from U.S. and Australia mammography data.
- 222.7% of women had BAC; prevalence rose to 61% for those over 70.
- 3Each 10-percentile BAC increase correlated to a 17% higher adjusted risk for major adverse cardiovascular events (MACE).
- 4Integration of BAC percentiles improved net risk reclassification by 5% and C-statistic from 0.67 to 0.71 (p = 0.04).
- 5Low and intermediate ASCVD risk women showed significantly increased MACE rates when BAC was above or below the median.
- 6Authors developed an online tool to aid clinical decision-making based on BAC percentiles.
Why It Matters
This study supports the use of mammogram-derived BAC scoring, automated by AI, to better stratify cardiovascular risk in women—potentially enabling earlier, more effective preventive care using routinely available imaging.
Source
AuntMinnie
Related News
•Radiology Business
Meta Funds Ohio State’s Radiology AI Innovation Projects
Meta will fund Ohio State researchers to explore AI-driven advancements in radiology, including chest X-ray reporting and disease prediction.
•AuntMinnie
ChatGPT-5 Performs Poorly at Detecting Distal Radius Fractures on X-rays
ChatGPT-5 missed about half of distal radius fractures on x-rays, showing limited diagnostic capability in a real-world scenario.
•AuntMinnie
AI Triage Tool Speeds Up ICH Head CT Turnaround in ED
AI-based triage for intracranial hemorrhage on head CT reduced emergency department report turnaround by 13 minutes.