Data science of stroke imaging and enlightenment of the penumbra.

Imaging protocols of acute ischemic stroke continue to hold significant uncertainties regarding patient selection for reperfusion therapy with thrombolysis and mechanical thrombectomy. Given that patient inclusion criteria can easily introduce biases that may be unaccounted for, the reproducibility and reliability of the patient screening method is of utmost importance in clinical trial design. The optimal imaging screening protocol for selection in targeted populations remains uncertain. Acute neuroimaging provides a snapshot in time of the brain parenchyma and vasculature. By identifying the at-risk but still viable penumbral tissue, imaging can help estimate the potential benefit of a reperfusion therapy in these patients. This paper provides a perspective about the assessment of the penumbral tissue in the context of acute stroke and reviews several neuroimaging models that have recently been developed to assess the penumbra in a more reliable fashion. The complexity and variability of imaging features and techniques used in stroke will ultimately require advanced data driven software tools to provide quantitative measures of risk/benefit of recanalization therapy and help aid in making the most favorable clinical decisions

Artificial Intelligence and Stroke Management

As a burgeoning technology, artificial intelligence has been utilized in numerous domains, including stroke prevention, diagnosis, treatment, and rehabilitation, and has demonstrated considerable promise. The combination of artificial intelligence and big data can be utilized for accurate identification of stroke high-risk groups, automatic etiology classification, and assistance in the formulation of acute stroke and secondary prevention strategies, thereby enhancing the rehabilitation treatment effect for stroke patients. This article discusses the accomplishments made in artificial intelligence research for stroke prevention, diagnosis, treatment, and rehabilitation

A Review on Computer Aided Diagnosis of Acute Brain Stroke.

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas

Artificial intelligence and cost-effectiveness analyses of radiological imaging in acute ischemic stroke

Acute ischemic stroke is caused by an occlusion of an artery in the brain. Current treatment options for acute ischemic stroke are intravenous thrombolysis and endovascular treatment. In this thesis, three parts describe varying analytical approaches to improve acute stroke care. Part I provides model-based health economic analyses of treatment decisions to improve acute stroke care. Specifically, the benefits of expedited endovascular treatment delivery and the use of CT perfusion for patient selection and occlusion detection are described. In Part II, prognostic imaging markers are studied. Deep learning-based quantification of white matter lesion volume in CT is compared to the radiologist-lead Fazekas scale for prognosticating functional outcome and intracranial hemorrhage occurrence. We studied if intravenous thrombolysis before endovascular treatment might be withheld based on increased risks for poor outcome and intracranial hemorrhage related to white matter lesion load. Furthermore, we evaluated thrombus volume, thrombus length, and thrombus radiomics as patient functional and endovascular treatment procedural outcome predictors. In Part III, we used generative adversarial networks to perform image-to-image translation. We translated CT scans with follow-up hemorrhagic or ischemic stroke lesions to baseline CT scans without. Furthermore, we remove contrast in CTA by translating a CTA to a non-contrast CT. Based on these translations we extract lesion segmentations in follow-up CT and vessel segmentations in CTA

Machine Learning in Acute Ischemic Stroke Neuroimaging

Machine Learning (ML) through pattern recognition algorithms is currently becoming an essential aid for the diagnosis, treatment, and prediction of complications and patient outcomes in a number of neurological diseases. The evaluation and treatment of Acute Ischemic Stroke (AIS) have experienced a significant advancement over the past few years, increasingly requiring the use of neuroimaging for decision-making. In this review, we offer an insight into the recent developments and applications of ML in neuroimaging focusing on acute ischemic stroke

Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis

BackgroundRecent studies have reported that machine learning (ML), with a relatively strong capacity for processing non-linear data and adaptive ability, could improve the accuracy and efficiency of prediction. The article summarizes the published studies on ML models that predict motor function 3–6 months post-stroke.MethodsA systematic literature search was conducted in PubMed, Embase, Cochorane and Web of Science as of April 3, 2023 for studies on ML prediction of motor function in stroke patients. The quality of the literature was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). A random-effects model was preferred for meta-analysis using R4.2.0 because of the different variables and parameters.ResultsA total of 44 studies were included in this meta-analysis, involving 72,368 patients and 136 models. Models were categorized into subgroups according to the predicted outcome Modified Rankin Scale cut-off value and whether they were constructed based on radiomics. C-statistics, sensitivity, and specificity were calculated. The random-effects model showed that the C-statistics of all models were 0.81 (95% CI: 0.79; 0.83) in the training set and 0.82 (95% CI: 0.80; 0.85) in the validation set. According to different Modified Rankin Scale cut-off values, C-statistics of ML models predicting Modified Rankin Scale>2(used most widely) in stroke patients were 0.81 (95% CI: 0.78; 0.84) in the training set, and 0.84 (95% CI: 0.81; 0.87) in the validation set. C-statistics of radiomics-based ML models in the training set and validation set were 0.81 (95% CI: 0.78; 0.84) and 0.87 (95% CI: 0.83; 0.90), respectively.ConclusionML can be used as an assessment tool for predicting the motor function in patients with 3–6 months of post-stroke. Additionally, the study found that ML models with radiomics as a predictive variable were also demonstrated to have good predictive capabilities. This systematic review provides valuable guidance for the future optimization of ML prediction systems that predict poor motor outcomes in stroke patients.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022335260, identifier: CRD42022335260