Artificial Intelligence Applications in Stroke

Mouridsen, Kim; Thurner, Patrick; Zaharchuk, Greg

doi:10.1161/strokeaha.119.027479

Cited by 83 publications

(70 citation statements)

References 38 publications

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“…In this regard, the sample size allowed us to apply a fivefold cross-validation, which prevents results from overfitting and guarantees replicability to other datasets. Moreover, the RF classifier is superior to most available learning algorithms, because it is easy to parameterize, robust against overfitting, not sensitive to noise in the dataset (i.e., good at dealing with outliers in training data), and able to avoid biases due to unrelated centers [ 37 ]. A potential limitation of this approach is that findings should be replicated in an independent set of subjects with comparable demographic, clinical and laboratory characteristics, to assess their generalizability.…”

Section: Discussionmentioning

confidence: 99%

SARS-CoV-2 infection and acute ischemic stroke in Lombardy, Italy

et al. 2021

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Objective To characterize patients with acute ischemic stroke related to SARS-CoV-2 infection and assess the classification performance of clinical and laboratory parameters in predicting in-hospital outcome of these patients. Methods In the setting of the STROKOVID study including patients with acute ischemic stroke consecutively admitted to the ten hub hospitals in Lombardy, Italy, between March 8 and April 30, 2020, we compared clinical features of patients with confirmed infection and non-infected patients by logistic regression models and survival analysis. Then, we trained and tested a random forest (RF) binary classifier for the prediction of in-hospital death among patients with COVID-19. Results Among 1013 patients, 160 (15.8%) had SARS-CoV-2 infection. Male sex (OR 1.53; 95% CI 1.06–2.27) and atrial fibrillation (OR 1.60; 95% CI 1.05–2.43) were independently associated with COVID-19 status. Patients with COVID-19 had increased stroke severity at admission [median NIHSS score, 9 (25th to75th percentile, 13) vs 6 (25th to75th percentile, 9)] and increased risk of in-hospital death (38.1% deaths vs 7.2%; HR 3.30; 95% CI 2.17–5.02). The RF model based on six clinical and laboratory parameters exhibited high cross-validated classification accuracy (0.86) and precision (0.87), good recall (0.72) and F1-score (0.79) in predicting in-hospital death. Conclusions Ischemic strokes in COVID-19 patients have distinctive risk factor profile and etiology, increased clinical severity and higher in-hospital mortality rate compared to non-COVID-19 patients. A simple model based on clinical and routine laboratory parameters may be useful in identifying ischemic stroke patients with SARS-CoV-2 infection who are unlikely to survive the acute phase.

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Section: Discussionmentioning

confidence: 99%

SARS-CoV-2 infection and acute ischemic stroke in Lombardy, Italy

et al. 2021

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“…The use of machine learning in stroke neuroimaging is already well-established, being utilized for determination of ischemic penumbra and large vessel occlusions (54). Emerging applications include the prediction of stroke onset time, functional outcomes following stroke, neurological deterioration, and hemorrhagic transformation (55). Future studies utilizing machine learning in infarct topography may offer additional, more accurate diagnostic tools for determination of stroke etiology.…”

Section: Discussionmentioning

confidence: 99%

Patterns of Infarction on MRI in Patients With Acute Ischemic Stroke and Cardio-Embolism: A Systematic Review and Meta-Analysis

et al. 2020

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Background: Cardioembolic strokes are common however atrial fibrillation, the most common cause, is often asymptomatic and difficult to detect. There is evidence that infarct topography and volume on magnetic resonance imaging may be associated with specific stroke etiologies.Aim: A systematic review and meta-analysis were undertaken to summarize the available evidence on the association between stroke etiology, infarct topography, and volume.Methods: A systematic review was conducted using Medline (OVID), Embase (OVID), and PubMed databases. Hand searches of the gray literature and of reference lists in relevant articles were also performed. A quality assessment was undertaken, based on the STROBE checklist. For each study, the number of patients with and without a CE source of stroke and infarct topography was collected and outcomes presented as odds ratios (OR) with 95% CI and p-values.Results: Four thousand eight hundred and seventy-three patients with ischemic stroke were included, of whom 1,559 were determined to have a CE source. Bilateral infarcts (OR 3.41; 95% CI 2.20–5.29; p < 0.0001) and multiple territory infarcts (OR 1.57; 95% CI 1.12–2.21; p = 0.009) were more common in patients with a CE source of stroke, than patients without a CE source. Lacunar infarcts (OR 0.49; 95% CI 0.31–0.80; p = 0.004) were more likely to occur in patients without a CE source. No significant difference between the frequency of multiple infarcts (OR 0.96; 95% CI 0.57–1.61; p = 0.87) anterior circulation (OR 1.45; 95% CI 0.83–2.53; p = 0.19) or posterior circulation infarcts (OR 1.06; 95% CI 0.72–1.57; p = 0.75), between the two groups were identified. Three out of four studies examining volume, found a significant association between increased infarct volume and CE source of stroke. A sensitivity analysis with cryptogenic and undetermined stroke sources assumed to be cardioembolic, did not alter the associations observed.Conclusion: The findings of this systematic review and meta-analysis are broadly consistent with previous literature and provide more robust evidence on the association between infarct topography, volume and stroke etiology. Our findings may assist with refining cardiac investigations for patients with cryptogenic stroke, based on infarct topography.

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“…Effectivity of various machine learning algorithms (e.g., linear regression, logistic regression, support vector machines, random forest, deep neural networks) are tested, but convolutional neural networks (CNNs) are commonly used for medical image classification [18].…”

Section: Discussionmentioning

confidence: 99%

“…Convolutional neural networks (CNNs) are increasingly often used for medical image classification [18]. Compared to acute stroke registry and analysis of Lausanne (ASTRAL), the deep neural network model was confirmed as the best form of tested machine learning algorithms to correctly predict outcomes in acute stroke patients, while random forest and logistic regression algorithms did not significantly differ (0.839 versus 0.888, 0.857, and 0.849) [19].…”

Section: Stroke Managementmentioning

confidence: 97%

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How to Improve the Management of Acute Ischemic Stroke by Modern Technologies, Artificial Intelligence, and New Treatment Methods

Zeleňák

Krajina

Meyer

et al. 2021

Life

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Stroke remains one of the leading causes of death and disability in Europe. The European Stroke Action Plan (ESAP) defines four main targets for the years 2018 to 2030. The COVID-19 pandemic forced the use of innovative technologies and created pressure to improve internet networks. Moreover, 5G internet network will be helpful for the transfer and collecting of extremely big databases. Nowadays, the speed of internet connection is a limiting factor for robotic systems, which can be controlled and commanded potentially from various places in the world. Innovative technologies can be implemented for acute stroke patient management soon. Artificial intelligence (AI) and robotics are used increasingly often without the exception of medicine. Their implementation can be achieved in every level of stroke care. In this article, all steps of stroke health care processes are discussed in terms of how to improve them (including prehospital diagnosis, consultation, transfer of the patient, diagnosis, techniques of the treatment as well as rehabilitation and usage of AI). New ethical problems have also been discovered. Everything must be aligned to the concept of “time is brain”.

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Artificial Intelligence Applications in Stroke

Cited by 83 publications

References 38 publications

SARS-CoV-2 infection and acute ischemic stroke in Lombardy, Italy

SARS-CoV-2 infection and acute ischemic stroke in Lombardy, Italy

Patterns of Infarction on MRI in Patients With Acute Ischemic Stroke and Cardio-Embolism: A Systematic Review and Meta-Analysis

How to Improve the Management of Acute Ischemic Stroke by Modern Technologies, Artificial Intelligence, and New Treatment Methods

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