Artificial Intelligence-Enhanced Perfusion Scoring Improves the Diagnostic Accuracy of Myocardial Perfusion Imaging

We previously demonstrated that a deep learning (DL) model of myocardial perfusion SPECT imaging improved accuracy for detection of obstructive coronary artery disease (CAD). We aimed to improve the clinical translatability of this artificial intelligence (AI) approach using the results to derive enhanced total perfusion deficit (TPD) and 17-segment summed scores. We used a cohort of patients undergoing myocardial perfusion imaging within 180 d of invasive coronary angiography. Obstructive CAD was defined as any stenosis of at least 70% or at least 50% in the left main coronary artery. We used per-vessel DL predictions to modulate polar map pixel scores. These transformed polar maps were then used to derive TPD-DL and summed stress score-DL. We compared diagnostic performance using area under the receiver operating characteristic curve (AUC). In the 555 patients held out for testing, the median age was 65 y (interquartile range, 57-73 y), and 381 (69%) were male. Obstructive CAD was present in 329 (59%) patients. The prediction performance for obstructive CAD of stress TPD-DL (AUC, 0.837; 95% CI, 0.804-0.870) was higher than AI prediction alone (AUC, 0.795; 95% CI, 0.758-0.831; = 0.005) and traditional stress TPD (AUC, 0.737; 95% CI, 0.696-0.778; < 0.001). Summed stress score-DL had the second highest prediction performance (AUC, 0.822; 95% CI, 0.788-0.857) and higher AUC than traditional quantitative summed stress score (AUC, 0.728; 95% CI, 0.686-0.769; < 0.001). At a threshold of 5%, the sensitivity and specificity of TPD rose from 72% to 79% and from 62% to 70%, respectively. Integrating AI predictions with traditional quantitative approaches leads to a simplified AI approach, presenting clinicians with familiar measures but operating with higher accuracy than traditional quantitative scoring. This approach may facilitate integration of new AI methods into clinical practice.