Table 2 Classification studies performing external validation
From: Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies
No. | First author | Title | Type of external testing | Number of patients | Results |
|---|---|---|---|---|---|
1 | Singh | Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning | External testing group | 20,401 patients in the training and internal testing group (5 sites) and 9,019 in the external testing group (2 different sites) | AUC: 0.73 |
2 | Betancur | Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT A Multicenter Study | Multicenter | Total of 1,638 patients (67% men) without known coronary artery disease, undergoing stress 99mTc-sestamibi or tetrofosmin MPI with new generation solid-state scanners in 9 different sites | Per patient AUC: 0.80 Per vessel: AUC: 0.76 |
3 | Nakajima | Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images: a Japanese multicenter study | Multicenter | 364 patients collected from nine hospitals served as the validation dataset | Stress defects AUC: 0.92 Stress-induced ischemia AUC: 0.90 for patients with old myocardial infarction based on rest defects AUC: 0.97 |
4 | Otaki | Clinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease | Multicenter | External testing was performed in 555 patients from 2 centers | AUC: 0.83 |
5 | Apostolopoulos | Multi-input deep learning approach for Cardiovascular Disease diagnosis using Myocardial Perfusion Imaging and clinical data | Image acquisition device variation | 98 patients | Accuracy: 79.16% |
6 | Hu | Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECTregistry | Multicenter | 1980 patients from 9 centres | Per-vessel AUC: 0.79 Per-patient AUC: 0.81 |
7 | Betancur | Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study | Division of dataset in 4 groups and performed a leave one-center-out cross-validation for each center. Overall predictions for each center were merged to have an overall estimation of the multicenter performance | 1160 patients | Per-patient AUC: 0.81 Per-vessel AUC: 0.77 |
8 | Otaki | Diagnostic Accuracy of Deep Learning for Myocardial Perfusion Imaging in Men and Women with a High-Efficiency Parallel-Hole-Collimated Cadmium-Zinc-Telluride Camera: multicenter study | Training and testing datasets included both men and women for prediction of obstructive CAD using repeated leave-one-center-out external validation (4 models built from 3 centers and tested in 4th center) | 1160 patients in 4 separate centers | Sensitivity: 82% in men, and 71% in women |