Closing the cardiovascular gender gap via AI: the EMABASK breast screening model, Basque Country
Abstract
Background: Cardiovascular disease is the leading female mortality cause but remains underdiagnosed due to gender bias in risk algorithms. With >80% participation, the Basque Breast Cancer Screening Programme offers a high-reach platform to detect subclinical atherosclerosis via Breast Arterial Calcifications (BAC), a sex-specific biomarker present in 10–12% of mammograms but systematically overlooked.
Objectives: To design and evaluate EMABASK, an AI-assisted BAC detection model aiming to close the cardiovascular gender gap via population-level risk stratification at zero patient burden, facilitating its future incorporation into routine clinical practice.
Methods: Pilot implementation study combining: (1) semi-quantitative BAC visual scoring in the Basque screening cohort; (2) multidisciplinary referral circuit design (Radiology–Primary Care–Cardiology) with high-specificity AI thresholds; (3) Health Technology Assessment (HTA) framework to evaluate cost-effectiveness using NRI indices and Basque demographic data.
Results: Analysis shows 65.35% of detected BAC cases occur in women aged 60–69 (overall cohort prevalence: 6.4%). Among participants, 2.18% present a high-risk profile (high BAC burden plus low mammographic density), the priority cohort for proactive follow-up. Projections indicate 11,000–18,000 women identified annually (∼3,000 high-risk). HTA modelling confirms near-zero marginal detection cost per patient, supporting scalability.
Conclusions: EMABASK provides a scalable, gender-responsive AI framework for cardiovascular screening. High adherence (>80%) and the standardised Basque mammographic network (320,000 women) enable population-wide quantification. Synergising with CARAMEL (Horizon Europe), this model offers an internationally replicable blueprint for evidence-based HTA, closing the gender gap via digital screening transformation.
Key messages: • EMABASK identifies ∼3,000 high-risk women per year currently invisible to cardiovascular systems — using only their routine mammogram. Zero extra cost, zero extra appointments, zero gender bias.
• Cost-neutrality requires 22 MACE/yr at AI scale. Using strict dual criteria (high-grade BAC + low density), our conservative pilot alone projects 300+ events annually—fifteen times above threshold.
Related
- Closing the gender gap in human papillomavirus protection: A survey of healthcare workers
- Disparities in Editorial Leadership of Highly Cited Cardiology Journals by Gender and Country Income Classification
- Vaccination as a Strategy for Cardiovascular Prevention: Closing a Gap in Clinical Practice
- Gender-sensitive health reporting in the EU and OECD: insights from a cross-country review
- Closing the Gap Between Risk and Recognition: Improving Delirium Screening in the Pediatric Intensive Care Unit.
BibTeX
@article{guzmanrevuelta2026closing,
title = {{Closing the cardiovascular gender gap via AI: the EMABASK breast screening model, Basque Country}},
author = {J Guzman-Revuelta and MI Portillo-Villares and JA Lopez-Ruiz and R Blanco-Mata and BI Martinez-Eguia and EI Gainza-Jauregui},
journal = {The European Journal of Public Health},
year = {2026},
month = oct,
doi = {10.1093/eurpub/ckag169.270},
url = {https://doi.org/10.1093/eurpub/ckag169.270},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Closing the cardiovascular gender gap via AI: the EMABASK breast screening model, Basque Country
AU - J Guzman-Revuelta
AU - MI Portillo-Villares
AU - JA Lopez-Ruiz
AU - R Blanco-Mata
AU - BI Martinez-Eguia
AU - EI Gainza-Jauregui
PY - 2026
DA - 2026/10/01
JO - The European Journal of Public Health
DO - 10.1093/eurpub/ckag169.270
UR - https://doi.org/10.1093/eurpub/ckag169.270
AB - Background: Cardiovascular disease is the leading female mortality cause but remains underdiagnosed due to gender bias in risk algorithms. With >80% participation, the Basque Breast Cancer Screening Programme offers a high-reach platform to detect subclinical atherosclerosis via Breast Arterial Calcifications (BAC), a sex-specific biomarker present in 10–12% of mammograms but systematically overlooked. Objectives: To design and evaluate EMABASK, an AI-assisted BAC detection model aiming to close the cardiovascular gender gap via population-level risk stratification at zero patient burden, facilitating its future incorporation into routine clinical practice. Methods: Pilot implementation study combining: (1) semi-quantitative BAC visual scoring in the Basque screening cohort; (2) multidisciplinary referral circuit design (Radiology–Primary Care–Cardiology) with high-specificity AI thresholds; (3) Health Technology Assessment (HTA) framework to evaluate cost-effectiveness using NRI indices and Basque demographic data. Results: Analysis shows 65.35% of detected BAC cases occur in women aged 60–69 (overall cohort prevalence: 6.4%). Among participants, 2.18% present a high-risk profile (high BAC burden plus low mammographic density), the priority cohort for proactive follow-up. Projections indicate 11,000–18,000 women identified annually (∼3,000 high-risk). HTA modelling confirms near-zero marginal detection cost per patient, supporting scalability. Conclusions: EMABASK provides a scalable, gender-responsive AI framework for cardiovascular screening. High adherence (>80%) and the standardised Basque mammographic network (320,000 women) enable population-wide quantification. Synergising with CARAMEL (Horizon Europe), this model offers an internationally replicable blueprint for evidence-based HTA, closing the gender gap via digital screening transformation. Key messages: • EMABASK identifies ∼3,000 high-risk women per year currently invisible to cardiovascular systems — using only their routine mammogram. Zero extra cost, zero extra appointments, zero gender bias. • Cost-neutrality requires 22 MACE/yr at AI scale. Using strict dual criteria (high-grade BAC + low density), our conservative pilot alone projects 300+ events annually—fifteen times above threshold.
SN - 1101-1262
SN - 1464-360X
ER -