Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT

Abstract

Objectives We evaluated the automatic prediction of obstructive disease from myocardial perfusion imaging (MPI) by deep learning as compared to total perfusion deficit (TPD). Background Deep convolutional neural networks trained with large multi-center population may provide improved prediction of per-patient and per-vessel coronary artery disease (CAD) from SPECT MPI. Methods 1638 patients (67% males) without known CAD, undergoing stress 99mTc-sestamibi or tetrofosmin MPI with new generation solid-state scanners in 9 different sites, with invasive coronary angiography performed within 6 months of MPI, were studied. Obstructive disease was defined as ≥ 70% narrowing of coronary arteries (≥ 50% for left main). Left ventricular (LV) myocardium was segmented using clinical nuclear cardiology software and verified by an expert reader. Stress TPD was computed using gender- and camera-specific normal limits. Deep learning was trained using raw and quantitative polar maps and evaluated for prediction of obstructive stenosis in a stratified 10-fold cross validation procedure. Results 1018 (62%) patients and 1797 out of 4914 (37%) arteries had obstructive disease. Area under the receiver operating characteristics curve (AUC) for disease prediction by deep learning was higher than for TPD (per-patient: 0.80 vs. 0.78, per-vessel 0.76 vs. 0.73, P < 0.01). With deep learning threshold set to the same specificity as TPD, per-patient sensitivity improved from 79.8% (TPD) to 82.3% (deep learning) (P < 0.05), and per-vessel sensitivity improved from 64.4% (TPD) to 69.8% (deep learning) (P < 0.01). Conclusions Deep learning has the potential to improve automatic interpretation of MPI as compared to current clinical methods.