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Related Experiment Video

Updated: Jul 19, 2026

MRI and PET in Mouse Models of Myocardial Infarction

MRI and PET in Mouse Models of Myocardial Infarction

Published on: December 19, 2013

Reimagining Thomas Lewis's perspective: using artificial intelligence tools to predict cardiovascular risk from

Rafail A Kotronias¹, Ikboljon Sobirov¹, Charalambos Antoniades²

¹Acute Multidisciplinary Imaging & Interventional Centre (AMIIC), NIHR Oxford Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.

Heart (British Cardiac Society)

|March 6, 2026

Summary

Artificial intelligence (AI) in cardiovascular imaging extracts biological insights from routine scans. This enables precision medicine by predicting cardiovascular events and personalizing therapy, improving patient outcomes.

Keywords:

Biomarkers Computed Tomography Angiography Diagnostic Imaging Inflammation Translational Medical Research

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Area of Science:

Cardiovascular Imaging
Precision Medicine
Artificial Intelligence

Background:

Artificial intelligence (AI) is transforming cardiovascular imaging from diagnostic tools into precision medicine instruments.
This evolution aligns with clinical science principles, integrating physiology, experimentation, and patient care.
AI extracts biological information from routine imaging, linking it with molecular data to understand disease mechanisms, predict outcomes, and personalize treatments.

Purpose of the Study:

To review the evolution of AI in cardiovascular imaging.
To highlight AI's role in precision cardiology and patient care.
To discuss the integration of imaging data with molecular profiles for non-invasive 'molecular biopsies'.

Main Methods:

Review of AI applications in cardiovascular imaging.
Illustrative example: Fat Attenuation Index (FAI) score and AI-Risk model.
Analysis of radiotranscriptomics and big data-driven approaches.

Main Results:

The Fat Attenuation Index (FAI) score measures coronary inflammation from coronary CT angiograms (CCTA), predicting cardiovascular events.
The AI-Risk model integrates FAI score, plaque extent (Duke score), and clinical factors for accurate cardiovascular risk prediction.
AI facilitates non-invasive 'molecular biopsies' through radiotranscriptomics, accelerating precision cardiology.

Conclusions:

AI represents a modern translation of clinical science, turning imaging data into actionable biological insights.
AI in cardiovascular imaging enhances risk stratification, clinical decision-making, and personalized therapy.
Ethical, regulatory, and environmental challenges of AI deployment require careful consideration.