Table 2.
Summary table of the application of artificial intelligence in the treatment of ischemic stroke.
| Author and year | Imaging modality | Dataset size | Methodology | Multi center | External validation | Clinical application | Evaluation metrics | Conclusion |
|---|---|---|---|---|---|---|---|---|
| Hyunna et al. (32) | MRI | 355 | SVM, RF, LR | No | No | Identify of Onset Time | RF: TPR = 75.8% FPR = 82.6% |
ML can predict the onset time, aiding doctors in choosing treatment plans. |
| Liang et al. (33) | MRI | 433 | LR, SVM | No | No | Identify of Onset Time | LR: AUC = 0.91 SVM: AUC = 0.90 |
Integrating different images can also improve prediction accuracy. |
| Zhu et al. (34) | MRI | 268 | EfficientNet-B0U | No | No | Identify of Onset Time | ACC = 80.5% TPR = 76.9% FPR = 84.0% |
DL shows higher accuracy than ML. |
| Wu et al. (38) | MRI | 2,770 | DeepMedic | Yes | Yes | Classification of Subtypes | Precision = 0.83 | AI based on imaging data can accurately classify stroke subtypes. |
| Zhang et al. (37) | MRI | 174 | RF + Radiomic | No | No | Classification of Subtypes | AUC = 0.936 | Combining ML with radiomics can diagnose stroke subtypes. |
TPR, Sensitivity; AUC, Area Under Curve; FPR, Specificity; SVM, Support Vector Machine; LR, logistic regression; RF, Random Forest; ML, Machine Learning; DL, Deep Learning.