{
  "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.\n\nObjectives: 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.\n\nMethods: 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.\n\nResults: 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.\n\nConclusions: 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.\n\nKey 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.\n\n• 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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "abstract",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "J Guzman-Revuelta"
    },
    {
      "name": "MI Portillo-Villares"
    },
    {
      "name": "JA Lopez-Ruiz"
    },
    {
      "name": "R Blanco-Mata"
    },
    {
      "name": "BI Martinez-Eguia"
    },
    {
      "name": "EI Gainza-Jauregui"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1vam37jqgpw7z.bib",
  "cited_by": 0,
  "doi": "10.1093/eurpub/ckag169.270",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13698314/",
  "id": "1vam37jqgpw7z",
  "issns": [
    "1101-1262",
    "1464-360X"
  ],
  "landing_url": "https://doi.org/10.1093/eurpub/ckag169.270",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "3. PARALLEL PROGRAMME"
    ],
    [
      "heading",
      "AcademicSubjects/MED00860"
    ],
    [
      "heading",
      "AcademicSubjects/SOC01210"
    ],
    [
      "heading",
      "AcademicSubjects/SOC02610"
    ],
    [
      "openalex",
      "2740|Digital Radiography and Breast Imaging"
    ]
  ],
  "openalex": "W7223685809",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13698314/pdf/?tool=EBI",
  "pmcid": "PMC13698314",
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Oxford University Press",
  "ris": "https://research.draconitic.com/paper/1vam37jqgpw7z.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:13Z",
      "native_id": "PMC13698314",
      "source": "pmc",
      "url": "https://doi.org/10.1093/eurpub/ckag169.270"
    },
    {
      "first_seen": "2026-10-30T12:21:34Z",
      "native_id": "PMC:PMC13698314",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13698314"
    }
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2740|Digital Radiography and Breast Imaging",
      "subfield": "general"
    }
  ],
  "title": "Closing the cardiovascular gender gap via AI: the EMABASK breast screening model, Basque Country",
  "title_text": "Closing the cardiovascular gender gap via AI: the EMABASK breast screening model, Basque Country",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:35:12Z",
  "url": "https://research.draconitic.com/paper/1vam37jqgpw7z",
  "venue": "The European Journal of Public Health",
  "versions": []
}