Multimodal magnetic resonance imaging to identify stroke onset within 6 h in patients with large vessel occlusions

Automatic triaging of acute ischemic stroke patients for reperfusion therapies using Artificial Intelligence methods and multiple MRI features: A review

BACKGROUND

The selection of appropriate treatments for Acute Ischemic Stroke (AIS), including Intravenous (IV) tissue plasminogen activator (tPA) and Mechanical thrombectomy, is a critical aspect of clinical decision-making. Timely treatment is essential, with recommended administration of therapies within 4.5 h of symptom onset. However, patients with unknown Time Since Stroke (TSS), are often excluded from thrombolysis, even if the stroke onset exceeds 6 h. Current clinical guidelines propose using multimodal Magnetic Resonance Imaging (MRI) to assess various mismatches.

METHODS

The review explores the significance of automatic methods based on Artificial Intelligence (AI) algorithms that utilize multiple MRI features to identify patients who are most likely to benefit from acute reperfusion therapies. These AI methods include TSS classification and patient selection for therapies in the late time window (>6 h) using MRI images to provide detailed stroke information.

RESULTS

The review discusses the challenges and limitations in the existing mismatch methods, which may lead to missed opportunities for reperfusion therapy. To address these limitations, AI approaches have been developed to enhance accuracy and support clinical decision-making. These AI methods have shown promising results, outperforming traditional mismatch assessments and providing improved sensitivity and specificity in identifying patients eligible for reperfusion therapies.

DISCUSSION

In summary, the integration of AI algorithms utilizing multiple MRI features has the potential to enhance accuracy, improve patient outcomes, and positively influence the decision-making process in AIS. However, ongoing research and collaboration among clinicians, researchers, and technologists are vital to realize the full potential of AI in optimizing stroke management.

The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis

BACKGROUND

Machine learning is a potentially effective method for identifying and predicting the time of the onset of stroke. However, the value of applying machine learning in this field remains controversial and debatable.

OBJECTIVE

We aimed to assess the value of applying machine learning in predicting the time of stroke onset.

METHODS

PubMed, Web of Science, Embase, and Cochrane were comprehensively searched. The C index and sensitivity with 95% CI were used as effect sizes. The risk of bias was evaluated using PROBAST (Prediction Model Risk of Bias Assessment Tool), and meta-analysis was conducted using R (version 4.2.0; R Core Team).

RESULTS

Thirteen eligible studies were included in the meta-analysis involving 55 machine learning models with 41 models in the training set and 14 in the validation set. The overall C index was 0.800 (95% CI 0.773-0.826) in the training set and 0.781 (95% CI 0.709-0.852) in the validation set. The sensitivity and specificity were 0.76 (95% CI 0.73-0.80) and 0.79 (95% CI 0.74-0.82) in the training set and 0.81 (95% CI 0.68-0.90) and 0.83 (95% CI 0.73-0.89) in the validation set, respectively. Subgroup analysis revealed that the accuracy of machine learning in predicting the time of stroke onset within 4.5 hours was optimal (training: 0.80, 95% CI 0.77-0.83; validation: 0.79, 95% CI 0.71-0.86).

CONCLUSIONS

Machine learning has ideal performance in identifying the time of stroke onset. More reasonable image segmentation and texture extraction methods in radiomics should be used to promote the value of applying machine learning in diverse ethnic backgrounds.

TRIAL REGISTRATION

PROSPERO CRD42022358898; https://www.crd.york.ac.uk/Prospero/display_record.php?RecordID=358898.

Predicting Clinical Outcomes in Acute Ischemic Stroke Patients Undergoing Endovascular Thrombectomy with Machine Learning : A Systematic Review and Meta-analysis

PURPOSE

Conventional predictive models are based on a combination of clinical and neuroimaging parameters using traditional statistical approaches. Emerging studies have shown that the machine learning (ML) prediction models with multiple pretreatment clinical variables have the potential to accurately prognosticate the outcomes in acute ischemic stroke (AIS) patients undergoing thrombectomy, and hence identify patients suitable for thrombectomy. This article summarizes the published studies on ML models in large vessel occlusion AIS patients undergoing thrombectomy.

METHODS

We searched electronic databases including PubMed from 1 January 2000 up to 14 October 2019 for studies that evaluated ML algorithms for the prediction of outcomes in stroke patients undergoing thrombectomy. We then used random-effects bivariate meta-analysis models to summarize the studies.

RESULTS

We retained a total of five studies that evaluated ML (4 support vector machine, 1 decision tree model) with a combined cohort of 802 patients. The prevalence of good functional outcome defined by 90-day mRS of 0-2 when available. Random effects model demonstrated that the AUC was 0.846 (95% confidence interval, CI 0.686-0.902). A pooled diagnostic odds ratio of 12.6 was computed. The pooled sensitivity and specificity were 0.795 (95% CI 0.651-0.889) and 0.780 (95% CI 0.634-0.879), respectively.

CONCLUSION

ML may be useful as an adjunct to clinical assessment to predict functional outcomes in AIS patients undergoing thrombectomy, and hence identify suitable patients for treatment. Further studies validating ML models in large multicenter cohorts are necessary to explore this further.