Table 2.
Included Studies on Diagnosis
| Category | Reference | Country (Income Status) | Quality Grade | Risk of Bias | Concern for Applicability | Artificial Intelligence Technique Used | Key Findings |
|---|---|---|---|---|---|---|---|
| Angiography | Rava et al., 202057 | United States (high) | Moderate | Low | Low | SVM | Most accurate classification of infarct regions was plotting mean transit time versus peak height and mean transmit time versus AUC |
| Reid et al., 201958 | Australia, Canada (high) | Moderate | Low | Low | SVM | mCTA-venous had a large effect on accurately identifying early ischemia when dichotomized for Alberta Stroke Program Early CT Score ≥6 versus <6 compared with the moderate effect of NCCT and mCTA-regional leptomeningeal score SVM identified mCTA-venous as the most important imaging covariate for predicting 24-hour National Institutes of Health Stroke Scale and 90-day modified Rankin Scale score |
|
| Sheth et al., 201959 | United States (high) | Moderate | Low | Low | CNN | CNN autonomously learned to identify the intracerebral vasculature on CTA and detected LVO with AUC 0.88 CNN determined infarct core as defined by CTP-RAPID from the CTA source images with AUC 0.88 for ischemic core ≤30 mL and 0.90 for ischemic core ≤50 mL. CNN corresponded with CTP-RAPID volumes |
|
| Stib et al., 202061 | United States (high) | Moderate | Low | Low | CNN | Single-phase CTA achieved an AUC of 0.74 with sensitivity of 77% and specificity of 71% Phases 1, 2, and 3 achieved greater AUC, sensitivity, and specificity and improved fit compared with single-phase CTA | |
| Su et al., 202062 | Netherlands (high) | Moderate | Low | Low | CNN | Accuracy of AUC of 0.8 and dichotomized collateral score accuracy of 0.9 Error comparable to interobserver variation and results comparable to 2 independent radiologists | |
| Computed Tomography | Fang et al., 202037 | China (upper middle) | Moderate | Unclear | Unclear | AdaBoost, ANN, SVM, RF | RF had the best performance Delay between stroke and randomization to treatment was related to infarct visible on computed tomography |
| Olive-Gadea et al., 202054 | Spain (high) | Moderate | Low | Low | CNN | AUC for the identification of LVO with Methinks LVO was 0.87, and improved to 0.91 with Methinks LVO+ | |
| Qiu et al., 202056 | Canada, Korea, Switzerland (high) | Moderate | Low | Low | CNN, RF | Algorithm-detected lesion volume correlated with the reference standard of expertcontoured lesion volume in acute DWI scans Mean difference between algorithmsegmented volume and the DWI volume was nonsignificant | |
| Vargas et al., 201965 | United States (high) | Moderate | Low | Low | CNN | Best model was able to achieve an accuracy of 85.8% on validation data AUC was 0.90 for right-sided deficits, 0.96 for left-sided deficits, and 0.93 for no deficits |
|
| You et al., 202072 | Hong Kong (high) | Moderate | Low | Low | RF, SVM, XGBoost | XGBoost model at the third level of evaluation achieved the best model performance on testing group Youden index, accuracy, sensitivity, specificity, F1 score, and AUC were 0.638, 0.800, 0.953, 0.684, 0.804, and 0.847, respectively |
|
| Miscellaneous | Erani et al., 202036 | United States (high) | Moderate | Low | Low | ANN | AUC of 0.864 and sensitivity of 76% at 80% specificity |
| Fitzgerald et al., 201939 | Canada, United States (high) | Moderate | High | Low | NA | Proportion of platelet-rich clots and percentage of platelet content higher in the large artery atherosclerosis group compared with the cardioembolic group Large artery atherosclerosis and cryptogenic cases had a similar proportion of platelet-rich clots |
|
| Keenan et al., 202046 | United States (high) | Moderate | Low | Low | NA | Cranial accelerometry was 65% sensitive and 87% specific. Adding asymmetric arm weakness increased specificity to 91% Exploratory analysis requiring asymmetric arm weakness before cranial accelerometry mode improved sensitivity to 91% and specificity to 93% and minimized false-positive and false-negative results | |
| Smith et al., 202060 | United States (high) | Moderate | Unclear | Unclear | NA | In most patients with LVO, head pulses showed little cardiac contraction correlation Using biometric data only, properly classified 15/19 patients with LVOs and 20/23 patients with non-LVO, with AUC of 0.79, sensitivity of 73%, and specificity of 87% |
|
| Thorpe et al., 202063 | United States (high) | Moderate | High | High | NA | Observed flow types provide the foundation for objective methods of real-time automated flow type classification | |
| Meier et al., 201949 | Switzerland (high) | Moderate | Low | Low | CNN | Strong correlations of lesion volumes and good spatial overlap of respective lesion segmentations between the CNN method and reference output CNN underestimated smaller lesion volumes, leading to disagreement between the CNN and reference method in 9% of patients |
|
| Subtype | Garg et al., 201940 | United States (high) | Moderate | Low | Low | Natural language processing with k-nearest neighbors, gradient boosting, RF, SVM, extra random trees, and XGBoost | Best machine-based classification achieved a k of 0.25 using radiology reports alone, 0.57 using progress notes alone, and 0.57 using combined data Machine-based classification agreed with rater classification in 40 of 50 cases (κ = 0.72) |
| Wu et al., 201970 | Multiple (high) | Moderate | Low | Low | CNN | Ensemble consisting of a mixture of large database and single-center CNNs performed best Automated and manual lesion determination correlated well |
SVM, support vector machine; AUC, area under the curve; mCTA, multiphase computed tomography angiography; NCCT, noncontrast computed tomography; CNN, convolutional neural network; CTA, computed tomography angiography; LVO, large-vessel occlusion; ANN, artificial neural network; RF, random forest; DWI, diffusion-weighted imaging; XGBoost, extreme gradient boost; NA, not applicable.