Key result
AI model integrating PET MPI parameters improves obstructive CAD diagnosis over physician scoring, achieving 0.83 AUC.
Why the study?
PET/CT for myocardial perfusion imaging provides multiple biomarkers that are often evaluated separately, warranting an integrated AI model to improve diagnosis of obstructive CAD.
Does an AI model integrating multiple PET MPI parameters improve the diagnostic accuracy for obstructive CAD compared to standard clinical scoring and individual quantitative metrics?
Observational (n=1,664)
Yes
Does an AI model integrating multiple PET MPI parameters improve the diagnostic accuracy for obstructive CAD compared to standard clinical scoring and individual quantitative metrics?
Effect estimate: AUC 0.83 (95% CI 0.81-0.85)
Absolute Event Rate: 0.83% vs 0.8%
p-value: p=0.02
An interpretable AI model integrating multiple PET MPI parameters significantly improves the diagnostic accuracy for obstructive CAD compared to standard clinical assessment by experienced physicians.
May aid PET MPI interpretation via AI integration; leaves open prospective validation before clinical use.
Background: Positron emission tomography (PET)/CT for myocardial perfusion imaging (MPI) provides multiple imaging biomarkers, often evaluated separately. We developed an artificial intelligence (AI) model integrating key clinical PET MPI parameters to improve the diagnosis of obstructive coronary artery disease (CAD). Methods: From 17,348 patients undergoing cardiac PET/CT across four sites, we retrospectively enrolled 1,664 subjects who had invasive coronary angiography within 180 days and no prior CAD. Deep learning was used to derive coronary artery calcium score (CAC) from CT attenuation correction maps. XGBoost machine learning model was developed using data from one site to detect CAD, defined as left main stenosis ≥50% or ≥70% in other arteries. The model utilized 10 image-derived parameters from clinical practice: CAC, stress/rest left ventricle ejection fraction, stress myocardial blood flow (MBF), myocardial flow reserve (MFR), ischemic and stress total perfusion deficit (TPD), transient ischemic dilation ratio, rate pressure product, and sex. Generalizability was evaluated in the remaining three sites-chosen to maximize testing power and capture inter-site variability-and model performance was compared with quantitative analyses using the area under the receiver operating characteristic curve (AUC). Patient-specific predictions were explained using shapley additive explanations. Results: There was a 61% and 53% CAD prevalence in the training (n=386) and external testing (n=1,278) set, respectively. In the external evaluation, the AI model achieved a higher AUC (0.83 [95% confidence interval (CI): 0.81-0.85]) compared to clinical score by experienced physicians (0.80 [0.77-0.82], p=0.02), ischemic TPD (0.79 [0.77-0.82], p<0.001), MFR (0.75 [0.72-0.78], p<0.001), and CAC (0.69 [0.66-0.72], p<0.001). The models' performances were consistent in sex, body mass index, and age groups. The top features driving the prediction were stress/ischemic TPD, CAC, and MFR. Conclusion: AI integrating perfusion, flow, and CAC scoring improves PET MPI diagnostic accuracy, offering automated and interpretable predictions for CAD diagnosis.
No takes yet. Share an insight, caveat, or question.
Zhang et al. (2025) conducted an observational in Suspected coronary artery disease (n=1,664). Artificial intelligence model integrating 10 clinical PET MPI parameters vs. Clinical score by experienced physicians was evaluated on Diagnosis of obstructive coronary artery disease (left main stenosis ≥50% or ≥70% in other arteries) (AUC 0.83, 95% CI 0.81-0.85, p=0.02). An artificial intelligence model integrating 10 clinical PET MPI parameters improved the diagnosis of obstructive coronary artery disease, achieving an AUC of 0.83 compared to 0.80 for clinical scoring by experienced physicians.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: