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AI-Enabled CT Cardiac Chamber Volumetry Predicts Atrial Fibrillation and Stroke Comparable to MRI

Morteza Naghavi1, Anthony P Reeves2, Kyle C Atlas1

  • 1HeartLung.AI, Houston, Texas, USA.

JACC. Advances
|January 1, 2025
PubMed

Insights

AI-CAC automated left atrial volumetry shows similar accuracy to expert measurements for predicting atrial fibrillation (AF) and stroke risk over 15 years. This AI tool offers a promising, efficient method for cardiovascular risk assessment.

Area of Science:

  • Cardiology
  • Artificial Intelligence in Medicine
  • Medical Imaging

Background:

  • Coronary artery calcium (CAC) scoring is a standard tool for cardiovascular risk assessment.
  • Artificial intelligence-based CAC (AI-CAC) analysis offers more actionable information than traditional Agatston CAC scoring.
  • Previous research demonstrated AI-CAC's ability to predict atrial fibrillation (AF) using automated left atrial (LA) volumetry.

Purpose of the Study:

  • To evaluate the performance of AI-CAC LA volumetry in predicting incident AF and stroke.
  • To compare AI-CAC LA volumetry with LA volume measured by human experts using cardiac magnetic resonance imaging (CMRI).
  • To assess AI-CAC's predictive capabilities against established risk factors like the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE-AF) risk score, Agatston score, and NT-proBNP.

Main Methods:

  • Utilized 15-year outcomes data from 3,552 asymptomatic individuals from the Multi-Ethnic Study of Atherosclerosis (MESA) cohort.
  • Compared AI-CAC automated LA volumetry with expert-measured CMRI LA volume for predicting AF and stroke.
  • Assessed predictive performance using time-dependent area under the curve (AUC) and continuous net reclassification index (NRI).

Main Results:

  • AI-CAC LA volumetry and expert CMRI LA volume demonstrated comparable predictive accuracy for AF (AUC 0.802 vs 0.798) and stroke (AUC 0.762 vs 0.751) over 15 years.
  • AI-CAC significantly improved 5-year AF prediction when added to the CHARGE-AF risk score, NT-proBNP, and Agatston score (continuous NRI: 0.23, 0.37, 0.44, respectively; P < 0.0001 for all).

Conclusions:

  • AI-CAC automated LA volumetry is a reliable method for predicting incident AF and stroke, performing similarly to expert-derived CMRI LA volume.
  • AI-CAC offers a valuable, potentially more efficient, tool for cardiovascular risk stratification.
  • Further research is warranted to explore the clinical utility of AI-CAC in AF and stroke prediction.
Abstract

Keywords:
artificial intelligenceatrial fibrillationcardiac magnetic resonance imagingcoronary artery calciumleft atrial volumestroke

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