[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120028-en":3,"doc-seo-120028-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120028,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",7,"Healthcare","Machine learning and acute stroke imaging","Machine learning (ML) has achieved strong performance in automated analysis of neuroimaging studies, and its influence in acute stroke imaging is expected to expand. Clinicians must learn how ML methods work, interpret ML outputs, and judge algorithm performance using appropriate evaluation strategies. This review summarizes common ML techniques for medical imaging analysis, outlines methods for assessing performance, and synthesizes literature on applications in acute ischemic stroke and related procedures, emphasizing clinically meaningful imaging results.","Author Manuscript Author Manuscript Author Manuscript Author Manuscript  \n\n|  | HHS Public Access\u003Cbr>Author manuscript\u003Cbr>J Neurointerv Surg. Author manuscript; available in PMC 2023 September 27. |\n| --- | --- |\n\nPublished in final edited form as:  \nJ Neurointerv Surg. 2023 February ; 15(2): 195–199. doi:10.1136/neurintsurg-2021-018142 .  \nMachine learning and acute stroke imaging  \nSunil A Sheth 1 , Luca Giancardo2 , Marco Colasurdo3,4 , Visish M Srinivasan5 , Arash Niktabe 1 , Peter Kan3  \n1 Department of Neurology, UTHealth McGovern Medical School, Houston, Texas, USA  \n2Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA  \n3 Department of Neurosurgery, The University of Texas Medical Branch at Galveston, Galveston, Texas, USA  \n4 Department of Neuroradiology, The University of Texas Medical Branch at Galveston, Galveston, Texas, USA  \n5 Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, USA  \nAbstract  \nBackground—In recent years, machine learning (ML) has had notable success in providing automated analyses of neuroimaging studies, and its role is likely to increase in the future. Thus, it is paramount for clinicians to understand these approaches, gain facility with interpreting ML results, and learn how to assess algorithm performance.  \nObjective—To provide an overview of ML, present its role in acute stroke imaging, discuss methods to evaluate algorithms, and then provide an assessment of existing approaches.  \nMethods—In this review, we give an overview of ML techniques commonly used in medical imaging analysis and methods to evaluate performance. We then review the literature for relevant publications. Searches were run in November 2021 in Ovid Medline and PubMed. Inclusion criteria included studies in English reporting use of artificial intelligence (AI), machine learning, or similar techniques in the setting of, and in applications for, acute ischemic stroke or mechanical thrombectomy. Articles that included image-level data with meaningful results and sound ML approaches were included in this discussion.  \nCorrespondence to: Dr Sunil A Sheth, Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA; [Sunil.A.Sheth@uth.tmc.edu](Sunil.A.Sheth@uth.tmc.edu).  \nContributors PK and SAS: study conception and design. MC and VMS: data collection. AN, PK, SAS, and LG: analysis and interpretation of results. All authors prepared the draft manuscript, reviewed the results, and approved the final version of the manuscript.  \nCompeting interests PK is on the editorial board of JNIS.  \nSupplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.  \nAdditional supplemental material for this paper are available online. To view these files, please visit the journal online ([http://](http://)[ ](http://)[dx.doi.org/10.1136/neurintsurg-2021-018142](dx.doi.org/10.1136/neurintsurg-2021-018142)).  \nAuthor Manuscript Author Manuscript Author Manuscript Author Manuscript  \nSheth et al. Page 2  \nResults—Many publications on acute stroke imaging, including detection of large vessel occlusion, detection and quantification of intracranial hemorrhage and detection of infarct core, have been published using ML methods. Imaging inputs have included non-con","cbCaieZHsjAlN2zx","https://ap.wps.com/l/cbCaieZHsjAlN2zx","pdf",409649,1,13,"English","en",105,"# Abstract\n# Overview of Machine Learning in Stroke Imaging\n## Objective and Scope\n## Methods and Literature Review\n## Results: Key ML Applications\n## Conclusions and Future Integration","[{\"question\":\"What is the purpose of this review on acute stroke imaging?\",\"answer\":\"To provide an overview of machine learning, explain its role in acute stroke imaging, describe how to evaluate algorithms, and assess existing approaches.\"},{\"question\":\"Which ML evaluation and inclusion criteria are used in the reviewed literature?\",\"answer\":\"The review includes English studies reporting artificial intelligence or machine learning in acute ischemic stroke or mechanical thrombectomy, focusing on studies with image-level data, meaningful results, and sound ML approaches.\"},{\"question\":\"What imaging inputs and stroke tasks are highlighted in the results?\",\"answer\":\"The review discusses ML publications using non-contrast head CT, CT angiography, and MRI for tasks such as detection of large vessel occlusion, intracranial hemorrhage detection and quantification, and infarct core detection.\"}]","Machine learning and acute stroke imaging | 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