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Cited by 45Cardiac Imaging and DiagnosticsHealth Promotion and Cardiovascular PreventionCardiovascular Function and Risk Factors

Charting the Unseen: How Non-Invasive Imaging Could Redefine Cardiovascular Prevention

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Trimarchi, Giancarlo, Pizzino, Fausto, Paradossi, Umberto, Gueli, Ignazio Alessio, Palazzini, Matteo, Gentile, Piero, Di Spigno, Francesco, Ammirati, Enrico +3 more·2024·Journal of Cardiovascular Development and Disease
DOI 10.3390/jcdd11080245PMC PMC11355021
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Abstract

Cardiovascular diseases (CVDs) remain a major global health challenge, leading to significant morbidity and mortality while straining healthcare systems. Despite progress in medical treatments for CVDs, their increasing prevalence calls for a shift towards more effective prevention strategies. Traditional preventive approaches have centered around lifestyle changes, risk factors management, and medication. However, the integration of imaging methods offers a novel dimension in early disease detection, risk assessment, and ongoing monitoring of at-risk individuals. Imaging techniques such as supra-aortic trunks ultrasound, echocardiography, cardiac magnetic resonance, and coronary computed tomography angiography have broadened our understanding of the anatomical and functional aspects of cardiovascular health. These techniques enable personalized prevention strategies by providing detailed insights into the cardiac and vascular states, significantly enhancing our ability to combat the progression of CVDs. This review focuses on amalgamating current findings, technological innovations, and the impact of integrating advanced imaging modalities into cardiovascular risk prevention, aiming to offer a comprehensive perspective on their potential to transform preventive cardiology.

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  • AI models detect subtle trends in cardiovascular markers and metabolic profiles that are indicative of elevated risk, even in patients who appear asymptomatic based on standard clinical evaluations.

    “AI models detects subtle trends in cardiovascular markers and metabolic profiles that are indicative of elevated risk, even in patients who appear asymptomatic based on standard clinical evaluations (101).”

    From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [101]

    Section 12 reports that AI can quantify imaging markers, including epicardial adipose tissue volume, and predict major cardiovascular events in asymptomatic patients. It does not establish that AI detects subtle trends in metabolic profiles or that these patients appeared low-risk under standard clinical evaluations. Section 3 separately notes limitations of conventional risk scores, but does not link that point to the AI findings. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Authors
Trimarchi, G., Pizzino, F., Paradossi, U., Gueli, I. A., Palazzini, M., Gentile, P., Di Spigno, F., Ammirati, E., Garascia, A., Tedeschi, A., & Aschieri, D.
Affiliations
  • University of Messina (IT)
  • Fondazione Toscana Gabriele Monasterio (IT)
  • Azienda Socio Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda (IT)
  • Cardiology Unit of Emergency Department, Guglielmo da Saliceto Hospital, 29121 Piacenza, Italy
  • Guglielmo da Saliceto Hospital (IT)
Corresponding author
Tedeschi, Andrea
ORCID
Venue
Journal of Cardiovascular Development and Disease
Publisher
MDPI AG
Published
2024-08-09
Volume
11
Issue
8
Pages
245
ISSN
2308-3425 (ISSN-L)
DOI
10.3390/jcdd11080245
PMC
PMC11355021
OpenAlex
W4401482534
Cited by
45
References
8
Citations by year
2026: 7 · 2025: 19 · 2024: 19
FWCI
2.93
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Cardiovascular Development and Disease (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies) (submittedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • AstraZeneca
  • Cytokinetics
  • Kiniksa Pharmaceuticals
  • Ministero della Salute (GR-2019–12368506, PNRR-MAD-2022-12376225)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Function and Risk Factors
Keywords
cardiovascular disease prevention, noninvasive cardiac imaging, echocardiography, cardiac magnetic resonance, coronary CT angiography, cardiovascular risk assessment, early disease detection, preventive cardiology, personalized prevention

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