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

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.