Table 1.
Principal fields of application of Machine Learning Models in Stroke Medicine
| Study title | Objective | ML-model | Conclusion and clinical application | Reference |
|---|---|---|---|---|
| Comparison of different machine learning approaches to model stroke subtype classification and risk prediction | Diagnosis of stroke subtypes and mortality | RF | Prediction of the stroke type and associated outcomes that a patient may face | Garcia-Temza et al., 2019 |
| Ischemic stroke identification based on EEG and EOG using ID convolutional neural network and batch normalization | Diagnosis of ischemic stroke through EEG | 1D CNN vs. various models (NB, Classification Tree, ANN, RF, kNN, LR) | The findings suggest that EEG has significant potential for differentiating individuals with stroke from the general population, highlighting its feasibility as a diagnostic tool. | Giri et al., 2016 |
| An automated detection method for the MCA dot sign of acute stroke in unenhanced CT | Identification of the MCA dot sign in non-contrast CT scans | SVM | Potential detection of the MCA dot sign of acute stroke on unenhanced CT images | Takahashi et al., 2014 |
| Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks | Automatically segment stroke lesions in DWI | CNN | AI-enabled automated segmentation of acute ischemic stroke lesions on DWI achieves high accuracy, aiding swift diagnosis and treatment decisions. | Chen et al., 2017; Bentley et al., 2014 |
| 3D convolutional neural networks applied to CT angiography in the detection of acute ischemic stroke. | Ischemic stroke detection | 3D CNN | 3D convolutional neural networks can effectively detect acute ischemic stroke lesions from CTA-SI, with contralateral hemisphere data aiding false positive reduction. | Öman et al., 2019 |
| Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning. | Prediction of final infarct volume | CNN | Deep convolutional neural network accurately predicts final lesion volume in acute ischemic stroke, enhancing personalized treatment planning. | Nielsen et al., 2018 |
| Automatic machine-learning-based outcome prediction in patients with primary intracerebral hemorrhage | Prediction of the functional outcome (Measured by mRS) at the 1st and 6th mon | RF | Machine learning technique using a random forest model accurately predicts functional outcomes in primary intracerebral hemorrhage patients at 1st and 6th mon, aiding clinical decisions and patient care. | Wang et al., 2019 |
| Machine learning-based model for prediction of outcomes in acute stroke. | Prediction of mRS score (0–2 vs. 3–6) at 90 d | DNN | Machine learning algorithms, especially the deep neural network, significantly improve the prediction of long-term outcomes in ischemic stroke patients compared to traditional scoring methods. | Heo et al., 2019 |
| Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study | Automated detection of head CT scan abnormalities | DNN | Deep learning algorithms accurately identify abnormalities in head CT scans. Possibility to automate the triage process for urgent cases. | Chilamkurthy et al., 2018 |
| Machine learning to predict mortality after rehabilitation among patients with severe stroke | Predicting 3-yr mortality in stroke patients | RF, ADA-B, GB | Machine learning algorithms outperformed logistic regression for predicting 3-yr mortality in stroke patients. | Scrutinio et al., 2020 |
ADA-B: AdaBoost; AI: artificial intelligence; ANN: artificial neural network; CNN: convolutional neural network; CT: computed tomography; CTA: computed tomography angiography; DNN: deep neural network; DWI: diffusion-weighted imaging; EEG: electroencephalography; GB: gradient boosting; kNN: k-nearest neighbors; LR: logistic regression; MCA: middle cerebral artery; ML: machine learning; mRS: modified Rankin scale; NB: Naive Bayes; RF: random forest; SI: source images; SVM: support vector machine.