Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review

Background and purposeAcute stroke caused by large vessel occlusions (LVOs) requires emergent detection and treatment by endovascular thrombectomy. However, radiologic LVO detection and treatment is subject to variable delays and human expertise, resulting in morbidity. Imaging software using artificial intelligence (AI) and machine learning (ML), a branch of AI, may improve rapid frontline detection of LVO strokes. This report is a systematic review of AI in acute LVO stroke identification and triage, and characterizes LVO detection software.MethodsA systematic review of acute stroke diagnostic-focused AI studies from January 2014 to February 2019 in PubMed, Medline, and Embase using terms: ‘artificial intelligence’ or ‘machine learning or deep learning’ and ‘ischemic stroke’ or ‘large vessel occlusion’ was performed.ResultsVariations of AI, including ML methods of random forest learning (RFL) and convolutional neural networks (CNNs), are used to detect LVO strokes. Twenty studies were identified that use ML. Alberta Stroke Program Early CT Score (ASPECTS) commonly used RFL, while LVO detection typically used CNNs. Image feature detection had greater sensitivity with CNN than with RFL, 85% versus 68%. However, AI algorithm performance metrics use different standards, precluding ideal objective comparison. Four current software platforms incorporate ML: Brainomix (greatest validation of AI for ASPECTS, uses CNNs to automatically detect LVOs), General Electric, iSchemaView (largest number of perfusion study validations for thrombectomy), and Viz.ai (uses CNNs to automatically detect LVOs, then automatically activates emergency stroke treatment systems).ConclusionsAI may improve LVO stroke detection and rapid triage necessary for expedited treatment. Standardization of performance assessment is needed in future studies.

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Ischemic stroke in Morocco: A systematic review

10.21203/rs.2.14651/v2 ◽

2019 ◽

Author(s):

Ahmed Kharbach ◽

Obtel Majdouline ◽

Laila Lahlou ◽

Jehanne Aasfara ◽

Nour Mekaoui ◽

...

Keyword(s):

Risk Factors ◽

Systematic Review ◽

Ischemic Stroke ◽

Mortality Rate ◽

Acute Stroke ◽

Influential Factors ◽

Stroke Treatment ◽

Male Predominance ◽

Prehospital Delay ◽

Acute Stroke Treatment

Abstract Background: The aim of this systematic review is to determine; the epidemiological and etiological profiles, the influential factors of the prehospital delay, thrombolysis management, the acute and 3-month mortality rate and the genetic aspect of ischemic stroke in Morocco.Methods: The present work is a systematic review that was conducted according to the recommendations of the "Preferred reporting items for systematic reviews and meta-analysis". We used Pubmed, Sciencedirect, Scopus, Clinicalkey, and Google scholar databases for the raking of the gray literature during the period between 2009 and 2018. The protocol of the review was registered in the PROSPERO register (CRD42018115206). These studies were analyzed based on: Age, sex ratio, risk factors, etiological profile according to Trial of ORG classification 10172 in Acute Stroke Treatment, prehospital delay average and it’s influential factors, thrombolyzed patients proportion, acute and 3-month mortality and the genetic factors of ischemic stroke in Morocco. Results: Twenty-nine (n = 29) studies were selected. The average age ranged from 49±15.2 to 67.3 ± 9.9 years old. Moreover, we reported male predominance within all ages in 13 studies. High blood pressure, diabetes, smoking and heart disease were the four identified main risk factors by these studies. Atherosclerosis and cardioembolic were the main described etiologies of cerebral ischemia, and the average prehospital time ranged from 26 to 61.9 hours. The proportion of thrombolysed patients ranged from 1.8% to 2.9%, the mortality rate varied in the acute phase from 3 to 13%, and the 3-month mortality ranged from 4.3 to 32.5%. It is also important to highlight that most of these studies have a reduced sample size, conducted in hospital environment, and no confidence interval was reported. Conclusions: Ischemic stroke is affecting more likely the young population with male predominance. Moreover, the long prehospital delay and the low proportion of thrombolysed patients are alarming. Indicating, thus, the need to investigate in depth the key factors influencing the access to care for Moroccan patients in order to improve the management of this neurologic deficit in Morocco. Key words: Ischemic stroke, Trial of ORG classification 10172 in Acute Stroke Treatment classification, prehospital delay, thrombolysis, Morocco.

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Diagnostic value of transcranial ultrasonography for selecting subjects with large vessel occlusion: a systematic review

The Ultrasound Journal ◽

10.1186/s13089-019-0143-6 ◽

2019 ◽

Vol 11 (1) ◽

Cited By ~ 1

Author(s):

Daria Antipova ◽

Leila Eadie ◽

Ashish Stephen Macaden ◽

Philip Wilson

Keyword(s):

Systematic Review ◽

Diagnostic Accuracy ◽

Acute Stroke ◽

Clinical Assessment ◽

Assessment Tools ◽

Large Vessel ◽

Cochrane Library ◽

Direct Transfer ◽

Large Vessel Occlusion ◽

Vessel Occlusion

Abstract Introduction A number of pre-hospital clinical assessment tools have been developed to triage subjects with acute stroke due to large vessel occlusion (LVO) to a specialised endovascular centre, but their false negative rates remain high leading to inappropriate and costly emergency transfers. Transcranial ultrasonography may represent a valuable pre-hospital tool for selecting patients with LVO who could benefit from rapid transfer to a dedicated centre. Methods Diagnostic accuracy of transcranial ultrasonography in acute stroke was subjected to systematic review. Medline, Embase, PubMed, Scopus, and The Cochrane Library were searched. Published articles reporting diagnostic accuracy of transcranial ultrasonography in comparison to a reference imaging method were selected. Studies reporting estimates of diagnostic accuracy were included in the meta-analysis. Results Twenty-seven published articles were selected for the systematic review. Transcranial Doppler findings, such as absent or diminished blood flow signal in a major cerebral artery and asymmetry index ≥ 21% were shown to be suggestive of LVO. It demonstrated sensitivity ranging from 68 to 100% and specificity of 78–99% for detecting acute steno-occlusive lesions. Area under the receiver operating characteristics curve was 0.91. Transcranial ultrasonography can also detect haemorrhagic foci, however, its application is largely restricted by lesion location. Conclusions Transcranial ultrasonography might potentially be used for the selection of subjects with acute LVO, to help streamline patient care and allow direct transfer to specialised endovascular centres. It can also assist in detecting haemorrhagic lesions in some cases, however, its applicability here is largely restricted. Additional research should optimize the scanning technique. Further work is required to demonstrate whether this diagnostic approach, possibly combined with clinical assessment, could be used at the pre-hospital stage to justify direct transfer to a regional thrombectomy centre in suitable cases.

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Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review

BMJ Health & Care Informatics ◽

10.1136/bmjhci-2020-100262 ◽

2021 ◽

Vol 28 (1) ◽

pp. e100262

Author(s):

Mustafa Khanbhai ◽

Patrick Anyadi ◽

Joshua Symons ◽

Kelsey Flott ◽

Ara Darzi ◽

...

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Social Media ◽

Natural Language Processing ◽

Natural Language ◽

Patient Experience ◽

Language Processing ◽

Performance Metrics ◽

Free Text ◽

Patient Feedback

ObjectivesUnstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of the literature on the use of natural language processing (NLP) and machine learning (ML) to process and analyse free-text patient experience data.MethodsDatabases were systematically searched to identify articles published between January 2000 and December 2019 examining NLP to analyse free-text patient feedback. Due to the heterogeneous nature of the studies, a narrative synthesis was deemed most appropriate. Data related to the study purpose, corpus, methodology, performance metrics and indicators of quality were recorded.ResultsNineteen articles were included. The majority (80%) of studies applied language analysis techniques on patient feedback from social media sites (unsolicited) followed by structured surveys (solicited). Supervised learning was frequently used (n=9), followed by unsupervised (n=6) and semisupervised (n=3). Comments extracted from social media were analysed using an unsupervised approach, and free-text comments held within structured surveys were analysed using a supervised approach. Reported performance metrics included the precision, recall and F-measure, with support vector machine and Naïve Bayes being the best performing ML classifiers.ConclusionNLP and ML have emerged as an important tool for processing unstructured free text. Both supervised and unsupervised approaches have their role depending on the data source. With the advancement of data analysis tools, these techniques may be useful to healthcare organisations to generate insight from the volumes of unstructured free-text data.

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Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18094499 ◽

2021 ◽

Vol 18 (9) ◽

pp. 4499

Author(s):

Anil Babu Payedimarri ◽

Diego Concina ◽

Luigi Portinale ◽

Massimo Canonico ◽

Deborah Seys ◽

...

Keyword(s):

Public Health ◽

Artificial Intelligence ◽

Machine Learning ◽

Systematic Review ◽

Public Transportation ◽

Prediction Models ◽

Positive Impact ◽

Health Interventions ◽

Public Health Interventions ◽

Commercial Activities

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.

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Systematic review protocol to assess artificial intelligence diagnostic accuracy performance in detecting acute ischaemic stroke and large-vessel occlusions on CT and MR medical imaging

BMJ Open ◽

10.1136/bmjopen-2020-043665 ◽

2021 ◽

Vol 11 (3) ◽

pp. e043665

Author(s):

Srinivasa Rao Kundeti ◽

Manikanda Krishnan Vaidyanathan ◽

Bharath Shivashankar ◽

Sankar Prasad Gorthi

Keyword(s):

Artificial Intelligence ◽

Systematic Review ◽

Diagnostic Accuracy ◽

Ischaemic Stroke ◽

Study Protocol ◽

Systematic Reviews ◽

Acute Ischaemic Stroke ◽

Meta Analysis ◽

Large Vessel ◽

Sufficient Data

IntroductionThe use of artificial intelligence (AI) to support the diagnosis of acute ischaemic stroke (AIS) could improve patient outcomes and facilitate accurate tissue and vessel assessment. However, the evidence in published AI studies is inadequate and difficult to interpret which reduces the accountability of the diagnostic results in clinical settings. This study protocol describes a rigorous systematic review of the accuracy of AI in the diagnosis of AIS and detection of large-vessel occlusions (LVOs).Methods and analysisWe will perform a systematic review and meta-analysis of the performance of AI models for diagnosing AIS and detecting LVOs. We will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols guidelines. Literature searches will be conducted in eight databases. For data screening and extraction, two reviewers will use a modified Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. We will assess the included studies using the Quality Assessment of Diagnostic Accuracy Studies guidelines. We will conduct a meta-analysis if sufficient data are available. We will use hierarchical summary receiver operating characteristic curves to estimate the summary operating points, including the pooled sensitivity and specificity, with 95% CIs, if pooling is appropriate. Furthermore, if sufficient data are available, we will use Grading of Recommendations, Assessment, Development and Evaluations profiler software to summarise the main findings of the systematic review, as a summary of results.Ethics and disseminationThere are no ethical considerations associated with this study protocol, as the systematic review focuses on the examination of secondary data. The systematic review results will be used to report on the accuracy, completeness and standard procedures of the included studies. We will disseminate our findings by publishing our analysis in a peer-reviewed journal and, if required, we will communicate with the stakeholders of the studies and bibliographic databases.PROSPERO registration numberCRD42020179652.

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Abstract NS6: To Rest or Mobilize... When to Start Early Mobilization in Acute Stroke: a Systematic Review

Stroke ◽

10.1161/str.47.suppl_1.ns6 ◽

2016 ◽

Vol 47 (suppl_1) ◽

Author(s):

Michelle Whaley ◽

Wendy Dusenbury ◽

Andrei V Alexandrov ◽

Georgios Tsivgoulis ◽

Anne W Alexandrov

Keyword(s):

Systematic Review ◽

Ischemic Stroke ◽

Acute Stroke ◽

Early Mobilization ◽

Future Research ◽

Cochrane Central Register ◽

Large Artery ◽

Stroke Subtype ◽

Vascular Insufficiency ◽

The Impact

Background: Recent nursing initiatives encourage early mobilization of neurocritical care patients, but whether this intervention can be safely generalized to acute stroke is debatable. We performed a systematic review of findings from recent studies to provide direction for patient management and future research. Methods: An exhaustive literature search was performed in Medline, SCOPUS and the Cochrane Central Register of Controlled Trials to identify published clinical trial research using a very early mobility intervention (within 24 hours) in acute ischemic stroke patients. The primary efficacy outcome supporting the search was neurologic disability reduction or improved functional outcomes, and the primary safety outcome was neurologic deterioration. Studies were critically reviewed for inclusion by 3 separate investigators, findings were synthesized, and an overall recommendation for very early mobilization use in acute stroke was assigned according to GRADE criteria. Results: We initially identified 12 papers focused on early mobilization in acute stroke; of these, 6 observational studies were excluded, 1 study was excluded due to an ambiguous population, and 3 studies were excluded due to first initial mobilization out of bed occurring greater than 24 hours after admission. Two prospective randomized outcome blinded evaluation (PROBE) studies were retained, consisting of a total 2160 patients; ischemic stroke subtype was not disclosed in either study, limiting an understanding of the impact of very early mobilization on small versus large artery occlusion. Slower mobilization occurring beyond the first 24 hours was associated with higher rates of favorable outcome (mRS 0-2) at 90 days, whereas very early mobilization within the first 24 hours was associated with a number needed to harm of 25. Conclusions: In acute stroke, evidence supports a rested approach to care within the first 24 hours of hospitalization (GRADE: Strong recommendation, high quality of evidence). Similar to acute myocardial infarction, vascular insufficiency experienced in stroke likely warrants a more guarded approach to mobility. Additional studies exploring timing beyond 24 hours and dose of mobility interventions are warranted in discreet populations.

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AI-Enabled Support System for Melanoma Detection and Classification

International Journal of Reliable and Quality E-Healthcare ◽

10.4018/ijrqeh.2021100104 ◽

2021 ◽

Vol 10 (4) ◽

pp. 58-75

Author(s):

Vivek Sen Saxena ◽

Prashant Johri ◽

Avneesh Kumar

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Skin Lesion ◽

Performance Metrics ◽

Similarity Index ◽

Skin Lesions ◽

Machine Learning Algorithms ◽

Lesion Area ◽

Melanoma Detection ◽

Grabcut Algorithm

Skin lesion melanoma is the deadliest type of cancer. Artificial intelligence provides the power to classify skin lesions as melanoma and non-melanoma. The proposed system for melanoma detection and classification involves four steps: pre-processing, resizing all the images, removing noise and hair from dermoscopic images; image segmentation, identifying the lesion area; feature extraction, extracting features from segmented lesion and classification; and categorizing lesion as malignant (melanoma) and benign (non-melanoma). Modified GrabCut algorithm is employed to generate skin lesion. Segmented lesions are classified using machine learning algorithms such as SVM, k-NN, ANN, and logistic regression and evaluated on performance metrics like accuracy, sensitivity, and specificity. Results are compared with existing systems and achieved higher similarity index and accuracy.

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Artificial Intelligence for Large Vessel Occlusion Stroke: A Systematic Review

World Neurosurgery ◽

10.1016/j.wneu.2021.12.004 ◽

2021 ◽

Author(s):

Nathan A. Shlobin ◽

Ammad A. Baig ◽

Muhammad Waqas ◽

Tatsat R. Patel ◽

Rimal H. Dossani ◽

...

Keyword(s):

Artificial Intelligence ◽

Systematic Review ◽

Large Vessel ◽

Large Vessel Occlusion ◽

Vessel Occlusion

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Prevalence of large vessel occlusion in patients presenting with acute ischemic stroke: a 10-year systematic review of the literature

Journal of NeuroInterventional Surgery ◽

10.1136/neurintsurg-2018-014239 ◽

2018 ◽

Vol 11 (3) ◽

pp. 241-245 ◽

Cited By ~ 22

Author(s):

Nikita Lakomkin ◽

Mandip Dhamoon ◽

Kirsten Carroll ◽

Inder Paul Singh ◽

Stanley Tuhrim ◽

...

Keyword(s):

Systematic Review ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Large Vessel ◽

Large Vessel Occlusion ◽

Review Of The Literature ◽

Qualitative Synthesis ◽

Vessel Occlusion ◽

Number Of Patients ◽

Meta Analyses

BackgroundAccurate assessment of the prevalence of large vessel occlusion (LVO) in patients presenting with acute ischemic stroke (AIS) is critical for optimal resource allocation in neurovascular intervention.ObjectiveTo perform a systematic review of the literature in order to identify the proportion of patients with AIS presenting with LVO on image analysis.MethodsA systematic review was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines in order to identify studies reporting LVO rates for patients presenting with AIS. Studies that included patients younger than 18 years, were non-clinical, or did not report LVO rates in the context of a consecutive AIS series were excluded. Characteristics regarding presentation, diagnosis, and LVO classification were recorded for each paper.ResultsSixteen studies, spanning a total of 11 763 patients assessed for stroke, were included in the qualitative synthesis. The majority (10/16) of articles reported LVO rates exceeding 30% in patients presenting with AIS. There was substantial variability in the LVO definitions used, with nine unique classification schemes among the 16 studies. The mean prevalence of LVO was 31.1% across all studies, and 29.3% when weighted by the number of patients included in each study.ConclusionsDespite the wide variability in LVO classification, the majority of studies in the last 10 years report a high prevalence of LVO in patients presenting with AIS. These rates of LVO may have implications for the volume of patients with AIS who may benefit from endovascular therapy.

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Epidemiological Surveillance of the Impact of the COVID-19 Pandemic on Stroke Care Using Artificial Intelligence

Stroke ◽

10.1161/strokeaha.120.031960 ◽

2021 ◽

Author(s):

Raul G. Nogueira ◽

Jason M. Davies ◽

Rishi Gupta ◽

Ameer E. Hassan ◽

Thomas Devlin ◽

...

Keyword(s):

Artificial Intelligence ◽

Acute Stroke ◽

Systems Of Care ◽

Ct Perfusion ◽

Stroke Care ◽

Large Vessel ◽

Large Vessel Occlusion ◽

Vessel Occlusion ◽

Number Of Patients ◽

The Impact

Background and Purpose: The degree to which the coronavirus disease 2019 (COVID-19) pandemic has affected systems of care, in particular, those for time-sensitive conditions such as stroke, remains poorly quantified. We sought to evaluate the impact of COVID-19 in the overall screening for acute stroke utilizing a commercial clinical artificial intelligence platform. Methods: Data were derived from the Viz Platform, an artificial intelligence application designed to optimize the workflow of patients with acute stroke. Neuroimaging data on suspected patients with stroke across 97 hospitals in 20 US states were collected in real time and retrospectively analyzed with the number of patients undergoing imaging screening serving as a surrogate for the amount of stroke care. The main outcome measures were the number of computed tomography (CT) angiography, CT perfusion, large vessel occlusions (defined according to the automated software detection), and severe strokes on CT perfusion (defined as those with hypoperfusion volumes >70 mL) normalized as number of patients per day per hospital. Data from the prepandemic (November 4, 2019 to February 29, 2020) and pandemic (March 1 to May 10, 2020) periods were compared at national and state levels. Correlations were made between the inter-period changes in imaging screening, stroke hospitalizations, and thrombectomy procedures using state-specific sampling. Results: A total of 23 223 patients were included. The incidence of large vessel occlusion on CT angiography and severe strokes on CT perfusion were 11.2% (n=2602) and 14.7% (n=1229/8328), respectively. There were significant declines in the overall number of CT angiographies (−22.8%; 1.39–1.07 patients/day per hospital, P <0.001) and CT perfusion (−26.1%; 0.50–0.37 patients/day per hospital, P <0.001) as well as in the incidence of large vessel occlusion (−17.1%; 0.15–0.13 patients/day per hospital, P <0.001) and severe strokes on CT perfusion (−16.7%; 0.12–0.10 patients/day per hospital, P <0.005). The sampled cohort showed similar declines in the rates of large vessel occlusions versus thrombectomy (18.8% versus 19.5%, P =0.9) and comprehensive stroke center hospitalizations (18.8% versus 11.0%, P =0.4). Conclusions: A significant decline in stroke imaging screening has occurred during the COVID-19 pandemic. This analysis underscores the broader application of artificial intelligence neuroimaging platforms for the real-time monitoring of stroke systems of care.

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