Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study

Leave-one-center-out cross-validation. Input stress MPI datasets are divided by center (4 in total). Four folds are built, each containing training sample made up of images from 3 centers and validation sample with images from remaining center. This procedure allows external validation of 4 DL models trained separately in each fold.

DL prediction of obstructive CAD from 4 externally validated models with merged data for 4 centers. Per-patient (A) and per-vessel (B) DL predictions of obstructive CAD from upright and supine images (DL, red) are compared with prediction of obstructive CAD by combined upright-supine TPD (cTPD, blue). AUC per center was externally validated using CAD scores from 4 different DL models (1 per center) with each model trained with data from other 3 centers. Red dotted line (bottom) shows overall multicenter AUC. CI = confidence interval; ROC = receiver operating characteristic.

Sensitivity for prediction of obstructive CAD. Per-patient DL prediction of obstructive CAD by DL computed from upright and supine MPI (DL, red) had higher sensitivity than prediction by cTPD (blue) and same sensitivity as on-site clinical readers (green). DL cutoff was set to 0.29, and cTPD cutoff was set to 0.62% to exhibit same specificity as normal or probably-normal clinical read. CI = confidence interval.

Prediction of obstructive CAD per subpopulation. REFINE-SPECT subpopulations were defined by sex (F, M), obesity (nonobese: body mass index < 30 kg/m², obese: body mass index ≥ 30 kg/m²), stress imaging activity (low: patients undergoing stress-first/stress-only MPI; standard: patients undergoing rest-first/2-d MPI), and stress test type (exercise, pharmacologic). Red dotted line shows overall multicenter AUC as reported in Figure 3. BMI = body mass index; CI = confidence interval.