[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121291-en":3,"doc-seo-121291-105":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":4,"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},121291,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Multimodal Machine Learning for Stroke Prognosis and Diagnosis - A Systematic Review","Stroke is a life-threatening condition that can cause death or major sensorimotor deficits. With clinical care involving heterogeneous modalities—medical images, bio-signals, and patient clinical data—multimodal machine learning has become increasingly important. This systematic review evaluates state-of-the-art multimodal methods for stroke prognosis and diagnosis using PRISMA-based selection, highlighting fusion strategies (early, joint, and late), and discussing less explored paradigms such as translation and alignment. It also analyzes dataset scale and modality types, and outlines challenges and recommendations for next-generation models.","Multimodal Machine Learning for Stroke Prognosis and Diagnosis: A Systematic Review  \nSaeed Shurrab , Alejandro Guerra-Manzanares , Amani Magid , Bartlomiej Piechowski-Jozwiak  , S. Farokh Atashzar , Senior Member, IEEE, and Farah E. Shamout   \n(Review paper)  \nAbstract—Stroke is a life-threatening medical condition that could lead to mortality or significant sensorimotor deficits. Various machine learning techniques have been successfully used to detect and predict stroke-related outcomes. Considering the diversity in the type of clinical modalities involved during management of patients with stroke, such as medical images, bio-signals, and clinical data, multimodal machine learning has become increasingly popular. Thus, we conducted a systematic literature review to understand the current status of state-of-the-art multimodal machine learning methods for stroke prognosis and diagnosis. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during literature search and selection, our results show that the most dominant techniques are related to the fusion paradigm, specifically early, joint and late fusion. We discuss opportunities to leverage other multimodal learning paradigms, such as multimodal translation and alignment, which are generally less explored. We also discuss the scale of datasets and types of modalities used to develop existing models, highlighting opportunities for the creation of more diverse multimodal datasets. Finally, we present ongoing challenges and provide a set of recommendations to drive the next generation of multimodal learning methods for improved prognosis and diagnosis of patients with stroke.  \nReceived 12 December 2023; revised 27 June 2024 and 30 July 2024; accepted 17 August 2024 . Date of publication 22 August 2024; date of current version 7 November 2024 . This work was supported by Tamkeen through NYU Abu Dhabi Research Enhancement Fund, in part by the NYUAD Center for AI & Robotics under Grant CG010, in part by the Center for Interacting Urban Networks under Grant CG001, in part by the Center for Cyber Security under Grant G1104, in part by ASPIRE, the Technology Program Management Pillar of Abu Dhabi’s Advanced Technology Research Council (ATRC), through ASPIRE Precision Medicine Research Institute Abu Dhabi (ASPIREPMRIAD) under Award VRI- 20-10. (Saeed Shurrab and Alejandro Guerra-Manzanares contributed equally to this work.) (Corresponding author: Farah E. Shamout.)  \nSaeed Shurrab, Alejandro Guerra-Manzanares, and Farah E. Shamout are with the Computer Engineering Division at New York University Abu Dhabi, Abu Dhabi 129188, UAE (e-mail: saeed.shurrab@ [nyu.edu](nyu.edu) ; [alejandro.guerra@nyu.edu](alejandro.guerra@nyu.edu) ; [farah.shamout@nyu.edu](farah.shamout@nyu.edu)).  \nAmani Magid is with the Science and Engineering library at New York University Abu Dhabi, Abu Dhabi 129188, UAE (e-mail: am6087@ [nyu.edu](nyu.edu)).  \nBartlomiej Piechowski-Jozwiak is with the Neurological Institute at Cleveland Clinic Abu Dhabi, Abu Dhabi 112412, UAE (e-mail: neuro[bart@gmail.com](bart@gmail.com)).  \nS. Farokh Atashzar is with the Electrical and Computer Engineering, Mechanical and Aerospace Engineering at New York University, New York, NY 11201 USA ([e-mail: f.atashzar@nyu.edu](e-mail: f.atashzar@nyu.edu)).  \nDigital Object Identifier 10.1109/JBHI.2024.3448238  \nIndex Terms—Stroke, multimodal clinical data, machine learning, deep learning.  \nI. INTRODUCTION  \nSTROKE is the most common form of cerebrovascular dis  \nease. It ranks as the second leading cause of death globally and the most common cause ofmajor cognitive and sensorimotor disability in adults [1] . Patients who survive their first stroke are highly expected to experience recurrence, which can range from days to years [2] . Moreover, stroke survivors are prone to functional loss and emotional side effects that negatively impact quality of life. The global stroke-related burden figures showe","cbCaidfaYOVdhytO","https://ap.wps.com/l/cbCaidfaYOVdhytO","pdf",6735186,1,16,"English","en",105,"# Introduction\n## Stroke diagnosis\n## Stroke prognosis\n# Systematic review approach\n## PRISMA-based search and selection\n# Multimodal learning paradigms\n## Fusion strategies (early, joint, late)\n## Translation and alignment\n# Datasets and modalities\n# Challenges and recommendations","[{\"question\":\"What problem does the review address?\",\"answer\":\"It reviews current state-of-the-art multimodal machine learning methods for stroke prognosis and diagnosis, where multiple clinical modalities are used together.\"},{\"question\":\"How were studies selected in the systematic review?\",\"answer\":\"The review followed PRISMA guidelines for literature search and selection.\"},{\"question\":\"Which multimodal techniques are most dominant in the field?\",\"answer\":\"The results indicate that fusion-based approaches dominate, specifically early, joint, and late fusion.\"}]","Multimodal Machine Learning for Stroke Prognosis and Diagnosis - A Systematic Review | PDF",1785734928,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"multimodal-machine-learning-for-stroke-prognosis-and-diagnosis-a-systematic-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/multimodal-machine-learning-for-stroke-prognosis-and-diagnosis-a-systematic-review/121291/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the review address?","Question",{"text":75,"@type":76},"It reviews current state-of-the-art multimodal machine learning methods for stroke prognosis and diagnosis, where multiple clinical modalities are used together.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were studies selected in the systematic review?",{"text":80,"@type":76},"The review followed PRISMA guidelines for literature search and selection.",{"name":82,"@type":73,"acceptedAnswer":83},"Which multimodal techniques are most dominant in the field?",{"text":84,"@type":76},"The results indicate that fusion-based approaches dominate, specifically early, joint, and late fusion.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]