AI-driven quantification of breast arterial calcification (BAC) on mammograms predicts risk of major cardiovascular events in women, independent of traditional risk scores.
Key Details
- 1Study used a transformer-based segmentation model to quantify BAC on mammograms from 123,762 women across Emory Healthcare and Mayo Clinic Enterprise.
- 2BAC severity (mild, moderate, severe) was associated with incrementally higher hazard ratios for major adverse cardiovascular events (up to HR 3.29 for severe BAC in the Emory cohort).
- 3Incidence of major cardiovascular events increased more than 8-fold from zero to severe BAC category in the Emory cohort.
- 4Dose-response noted: each 1 mm² increase in BAC conferred an additional 2–3% risk for major events (p < 0.001).
- 5AI-derived BAC scores add prognostic value beyond the PREVENT cardiovascular risk calculator, offering a non-invasive risk assessment using existing mammogram data.
Why It Matters
Opportunistically assessing cardiovascular risk in women using routine mammograms through AI enables earlier prevention without extra imaging or radiation, potentially transforming population health strategies and radiology's role in preventive care.
Source
AuntMinnie
Related News
•Radiology Business
Real-World Study: Radiology AI Best in Emergency and Inpatient Settings
A commercial AI tool for intracranial aneurysm detection outperformed in inpatient and emergency settings but yielded limited benefits for outpatients in a major health system study.
•Radiology Business
New Rubric Enhances Safety of AI-Generated Radiology Summaries
Researchers developed a five-factor rubric to assess the safety and quality of AI-generated, patient-friendly radiology report summaries.
•Radiology Business
UCLA Review Evaluates Breast AI for Early Cancer Detection
A UCLA-led review analyzes current evidence for AI tools in screening mammography, focusing on their capability to detect interval cancers.