Table 1.
Clinical applications of DL in MPI diagnosis
| Authors | Year | Total N | Site(s) | DL | Input variables | Comparison | Evaluation |
|---|---|---|---|---|---|---|---|
| Nathalia Spier et al. | 2019 | 946 | 1 | GCNNs | rest and stress polar maps | human observer | agreement 83.1% |
| Mehdi Amini et al. | 2023 | 395 | 1 | classification | rest and stress MPI radiomics features, clinical features | no CAD vs. CAD, and low-risk/high-risk CAD | AUC 0.61 vs. 0.79 |
| Ting-Yi Su et al. | 2023 | 694 | 1 | 3D-CNN | resting-state images | SSS, SDS, and SRS | accuracy 87.08% sensitivity 86.49% specificity 87.41% |
| Ioannis D. Apostolopoulos et al. | 2021 | 566 | 1 | CNNs | AC and NAC polar maps, clinical data | medical expert | accuracy 79.15% sensitivity 89.17% specificity 71.20% |
| Papandrianos et al.9 | 2022 | 842 | 1 | RGB-CNN | SA, HLA, and VLA images | VGG-16 and DenseNet-121 model | accuracy 88.54% and 86.11% |
| R Rahmani et al. | 2019 | 923 | 1 | ANN | stress and rest polar plots, age, gender, and the number of risk factors | result of angiography, obstructive CAD, and Gensini score | accuracy 92.9% vs. 85.7% vs. 92.9% |
| Papandrianos et al.9 | 2022 | 314 | 1 | RGB-CNN, VGG-16 | stress and rest polar maps in AC and NAC format | TPD | accuracy 92.07% vs. 95.83% |
| Betancur et al.8 | 2018 | 1,638 | 9 | Deep CNN | raw and quantitative polar maps | TPD | AUC per patient 0.80 vs. 0.78 per vessel 0.76 vs. 0.73 |
| Otaki et al.10 | 2022 | 3,578 | 5 | CNNs | stress myocardial perfusion, wall motion, and wall thickening maps; left ventricular volumes, age, and sex | TPD and physician diagnosis | AUC 0.84 vs. 0.78 vs. 0.71 |
| Betancur et al.11 | 2019 | 1,160 | 4 | Deep CNN | upright and supine polar MPI maps | combined TPD | AUC per patient 0.81 vs. 0.78 per vessel 0.77 vs. 0.73 |
| Miller et al.12 | 2022 | 240 | 1 | Deep CNN | stress myocardial perfusion, wall motion, and wall thickening maps; left ventricular volumes, age, and sex | physician interpretation without CAD-DL and TPD | AUC 0.78 vs. 0.75 vs. 0.72 |
| Kenichi Nakajima et al. | 2017 | 1,001 | 12 | ANN | stress, rest, and difference features | SSS, SDS, and SRS | AUC SSS 0.92 vs. 0.82 SDS 0.90vs 0.75 SRS 0.97vs 0.91 |
| Liu et al.13 | 2021 | 37,243 | 1 | Deep CNN | stress-only circumferential count profile maps, gender, BMI, length, stress type, radio tracer, and the AC option | quantitative defect size (DS) | AUC 0.87 vs. 0.84 |
| Berkaya et al.14 | 2020 | 192 | 1 | DNNs | SA, HLA, and VLA slices | two expert readers | accuracy 94% sensitivity 88% specificity 100% |
| Teuho et al.15 | 2024 | 138 | 1 | Deep CNN | PET-CT, CTA, and clinical data | ICA | AUC 0.85 |
| Kusumoto et al.16 | 2024 | 5,443 | 1 | 3D-CNN | SA, HLA, and VLA images; age and sex | medical expert | accuracy 88% |