[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-290329-en":3,"detail-sidebar-cat-0-en-105":30,"doc-seo-290329-105":80},{"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":11,"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,"preview_page_count":4},290329,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine learning applications in stroke medicine - advancements, challenges, and future prospectives","Stroke is a leading cause of disability and mortality worldwide, driving the need for advanced technologies to strengthen diagnosis, treatment, and patient outcomes. Machine learning techniques have become promising tools in stroke medicine, supporting efficient analysis of large-scale datasets and enabling personalized, precision approaches. The review summarizes applications across imaging, subtype classification, risk stratification, treatment guidance, and prognosis prediction, while addressing standardization, validation, generalization, privacy, bias, clinical workflow integration, and regulation.","Machine learning applications in stroke medicine: advancements, challenges, and future prospectives  \nMario Daidone1, 2, *, \\#, Sergio Ferrantelli1, \\#, Antonino Tuttolomondo1  \n\n| [https://doi.org/10.4103/1673-5374.382228](https://doi.org/10.4103/1673-5374.382228) | Abstract\u003Cbr>Stroke is a leading cause of disability and mortality worldwide, necessitating the development of advanced technologies to improve its diagnosis, treatment, and patient outcomes. In recent years, machine learning techniques have emerged as promising tools in stroke medicine, enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches.\u003Cbr>This abstract provides a comprehensive overview of machine learning’s applications, challenges, and future directions in stroke medicine. Recently introduced machine learning algorithms\u003Cbr>have been extensively employed in all the fields of stroke medicine. Machine learning models\u003Cbr>have demonstrated remarkable accuracy in imaging analysis, diagnosing stroke subtypes, risk stratifications, guiding medical treatment, and predicting patient prognosis. Despite the tremendous potential of machine learning in stroke medicine, several challenges must be addressed. These include the need for standardized and interoperable data collection, robust model validation and generalization, and the ethical considerations surrounding privacy and bias. In addition, integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care. Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis, tailored treatment selection, and improved prognostication. Continued research and collaboration among clinicians, researchers, and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care, ultimately leading to enhanced patient outcomes and quality of life. This review aims to summarize all the current implications of machine learning in stroke diagnosis, treatment, and prognostic evaluation. At the same time, another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease.\u003Cbr>Key Words: cerebrovascular disease; deep learning; machine learning; reinforcement learning; stroke; stroke therapy; supervised learning; unsupervised learning |  |\n| --- | --- | --- |\n| Date of submission: March 7, 2023 |  |  |\n| Date of decision: June 28, 2023 |  |  |\n| Date of acceptance: July 11, 2023 |  |  |\n| Date of web publication: August 14, 2023\u003Cbr>From the Contents\u003Cbr>Introduction 769 Search Strategy 769 Current Evidence on Stroke 769 Machine Learning: New Light on Stroke 770 Machine Learning in Stroke Medicine: Current Research and Development 771 Conclusion 773 |  |  |\n| Introduction\u003Cbr>Stroke is one of the most common and devastating disorders, a leading cause of disability, and the second leading cause of death worldwide cause, with approximately 5.5 million deaths worldwide in 2015. Numerous advances have been made in understanding cerebrovascular diseases in recent years, but scientific research can still provide many improvements. Indeed, many steps remain to better characterize pathogenesis at the molecular level, identify risk profiles, prognostic stratification, and personalize treatment. Nevertheless, the significant advances in technology, which are increasingly beginning to have a place in the most modern approach to medicine, and the better knowledge of the pathophysiology of stroke open up new prospects for the development timing-based instrumental approach (Feigin et al., 2022) .\u003Cbr>Machine learning (ML) techniques have gained recognition in stroke medicine as valuable tools. They allow for the adequate examination of extensive datasets and support personalized and precise medical approaches . This summary thoroug","cbCaidRNnpglRnZJ","https://ap.wps.com/l/cbCaidRNnpglRnZJ","pdf",1293018,1,5,"English","en",105,"# Introduction\n## Search Strategy\n## Current Evidence on Stroke\n## Pathophysiology of stroke\n# Machine Learning: New Light on Stroke\n# Machine Learning in Stroke Medicine: Current Research and Development\n# Conclusion","[{\"question\":\"What are the main applications of machine learning in stroke medicine described in the document?\",\"answer\":\"Machine learning is used for imaging analysis, stroke subtype diagnosis, risk stratification, guiding medical treatment decisions, and predicting patient prognosis.\"},{\"question\":\"What challenges must be addressed before machine learning can be widely adopted in stroke care?\",\"answer\":\"Key challenges include standardized and interoperable data collection, robust model validation and generalization, and ethical concerns related to privacy and bias. Integration into clinical workflows and regulatory frameworks are also considered critical.\"},{\"question\":\"How were the literature searches conducted in this review?\",\"answer\":\"Searches were performed in September 2022 across CINAHL, Web of Science, and PubMed, with PubMed MeSH terms adapted for CINAHL and supplemented with relevant free-text terms. Results were limited to English-language articles from the last ten years, with additional records identified via reference list review.\"}]","Machine learning applications in stroke medicine - advancements, challenges, and future prospectives | PDF",1789641385,13,{"code":4,"msg":5,"data":31},[32,37,42,47,51,56,61,64,69,72,76],{"id":20,"doc_module":4,"doc_module_name":33,"category_name":34,"show_sort_weight":35,"slug":36},"Document","Story & Novel",90,"story-novel",{"id":38,"doc_module":4,"doc_module_name":33,"category_name":39,"show_sort_weight":40,"slug":41},2,"Literature",80,"literature",{"id":43,"doc_module":4,"doc_module_name":33,"category_name":44,"show_sort_weight":45,"slug":46},4,"Exam",70,"exam",{"id":21,"doc_module":4,"doc_module_name":33,"category_name":48,"show_sort_weight":49,"slug":50},"Comic",60,"comic",{"id":52,"doc_module":4,"doc_module_name":33,"category_name":53,"show_sort_weight":54,"slug":55},6,"Technology",50,"technology",{"id":57,"doc_module":4,"doc_module_name":33,"category_name":58,"show_sort_weight":59,"slug":60},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":33,"category_name":12,"show_sort_weight":62,"slug":63},30,"research-report",{"id":65,"doc_module":4,"doc_module_name":33,"category_name":66,"show_sort_weight":67,"slug":68},9,"Religion & Spirituality",20,"religion-spirituality",{"id":67,"doc_module":4,"doc_module_name":33,"category_name":70,"show_sort_weight":67,"slug":71},"World Cup","world-cup",{"id":73,"doc_module":4,"doc_module_name":33,"category_name":74,"show_sort_weight":73,"slug":75},10,"Lifestyle","lifestyle",{"id":77,"doc_module":4,"doc_module_name":33,"category_name":78,"show_sort_weight":21,"slug":79},19,"General","general",{"code":4,"msg":81,"data":82},"ok",{"site_id":24,"language":23,"slug":83,"title":13,"keywords":84,"description":14,"schema_data":85,"social_meta":139,"head_meta":141,"extra_data":143,"updated_unix":28},"machine-learning-applications-in-stroke-medicine-advancements-challenges-and-future-prospectives","",{"@graph":86,"@context":138},[87,101,121],{"@type":88,"itemListElement":89},"BreadcrumbList",[90,94,96,99],{"item":91,"name":92,"@type":93,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":95,"name":33,"@type":93,"position":38},"https://docshare.wps.com/document/",{"item":97,"name":12,"@type":93,"position":98},"https://docshare.wps.com/document/research-report/",3,{"item":100,"name":13,"@type":93,"position":43},"https://docshare.wps.com/document/machine-learning-applications-in-stroke-medicine-advancements-challenges-and-future-prospectives/290329/",{"url":100,"name":13,"@type":102,"image":103,"author":108,"headline":13,"publisher":110,"fileFormat":113,"inLanguage":23,"description":14,"dateModified":114,"datePublished":115,"encodingFormat":113,"isAccessibleForFree":116,"interactionStatistic":117},"DigitalDocument",{"url":104,"@type":105,"width":106,"height":107},"https://docshare.wps.com/thumbnails/machine-learning-applications-in-stroke-medicine-advancements-challenges-and-future-prospectives/290329.png","ImageObject",300,407,{"name":9,"@type":109},"Person",{"url":91,"name":111,"@type":112},"DocShare","Organization","application/pdf","2026-10-17","2026-09-17",true,{"@type":118,"interactionType":119,"userInteractionCount":11},"InteractionCounter",{"@type":120},"ViewAction",{"@type":122,"mainEntity":123},"FAQPage",[124,130,134],{"name":125,"@type":126,"acceptedAnswer":127},"What are the main applications of machine learning in stroke medicine described in the document?","Question",{"text":128,"@type":129},"Machine learning is used for imaging analysis, stroke subtype diagnosis, risk stratification, guiding medical treatment decisions, and predicting patient prognosis.","Answer",{"name":131,"@type":126,"acceptedAnswer":132},"What challenges must be addressed before machine learning can be widely adopted in stroke care?",{"text":133,"@type":129},"Key challenges include standardized and interoperable data collection, robust model validation and generalization, and ethical concerns related to privacy and bias. Integration into clinical workflows and regulatory frameworks are also considered critical.",{"name":135,"@type":126,"acceptedAnswer":136},"How were the literature searches conducted in this review?",{"text":137,"@type":129},"Searches were performed in September 2022 across CINAHL, Web of Science, and PubMed, with PubMed MeSH terms adapted for CINAHL and supplemented with relevant free-text terms. Results were limited to English-language articles from the last ten years, with additional records identified via reference list review.","https://schema.org",{"og:url":100,"og:type":140,"og:title":13,"og:site_name":111,"og:description":14},"article",{"robots":142,"canonical":100},"index,follow",{"doc_id":7,"site_id":24}]