Artificial intelligence–based quantification of breast arterial calcifications to predict cardiovascular morbidity and mortality
Authors' conclusion
Automatically quantified BAC is an independent predictor of MACE and mortality, adding prognostic value to the PREVENT score.
Study context
- Population
- Women with screening mammograms, racially diverse, 2 healthcare systems; Breast arterial calcification (BAC) quantified by AI (transformer-based neural network)
- Participants
- 123,762 women
- Exposure
- Mild, moderate and severe BAC vs zero BAC on screening mammography
- Outcome
- MACE
Methodological detailsmulti-institutional, two healthcare systems (internal and external cohorts)
- Multi-institutional
- Two healthcare systems (internal and external cohorts)
Main finding
As reported in the abstract
Any MACE (PREVENT-adjusted)
Each group vs zero BACHazard ratio (95% CI)
Mild BACinternal cohort
1.32(1.10–1.59)
Mild BACexternal cohort
1.28(1.17–1.39)
Moderate BACinternal cohort
1.75(1.23–2.50)
Moderate BACexternal cohort
1.79(1.55–2.06)
Severe BACinternal cohort
3.29(2.15–5.05)
Severe BACexternal cohort
2.80(2.36–3.32)
- Each 1 mm² increase in BAC: additional 2%-3% risk for major adverse cardiovascular events (MACE)
- P < 0.001
Observational data: associations, not effects.
Further findings
BAC prevalence
- BAC detected, internal cohort
- 16.1%
- BAC detected, external cohort
- 20.6%
Original publication
Artificial intelligence–based quantification of breast arterial calcifications to predict cardiovascular morbidity and mortality. Eur Heart J. Published 9 March 2026. doi:10.
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