{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T04:45:34Z","timestamp":1774673134523,"version":"3.50.1"},"reference-count":126,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,7,4]],"date-time":"2024-07-04T00:00:00Z","timestamp":1720051200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PRR\u2014Recovery and Resilience Plan and the European Union","award":["RE-C05-i02"],"award-info":[{"award-number":["RE-C05-i02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care.<\/jats:p>","DOI":"10.3390\/s24134355","type":"journal-article","created":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T03:48:56Z","timestamp":1720151336000},"page":"4355","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0590-4861","authenticated-orcid":false,"given":"Jo\u00e3o N. D.","family":"Fernandes","sequence":"first","affiliation":[{"name":"INESC TEC, 4200-465 Porto, Portugal"},{"name":"Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"},{"name":"Department of Computer Engineering, Superior Institute of Engineering of Porto, 4249-015 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4886-2406","authenticated-orcid":false,"given":"Vitor E. M.","family":"Cardoso","sequence":"additional","affiliation":[{"name":"Collaborative Laboratory for the Future Built Environment (BUILT CoLAB), Rua Do Campo Alegre, 760, 4150-003 Porto, Portugal"},{"name":"Department of Computer Engineering, Superior Institute of Engineering of Porto, 4249-015 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3409-8996","authenticated-orcid":false,"given":"Alberto","family":"Comesa\u00f1a-Campos","sequence":"additional","affiliation":[{"name":"Department of Design in Engineering, University of Vigo, 36312 Vigo, Spain"},{"name":"Design, Expert Systems and Artificial Intelligent Solutions Group (DESAINS), Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36312 Vigo, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4974-5946","authenticated-orcid":false,"given":"Alberto","family":"Pinheira","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Superior Institute of Engineering of Porto, 4249-015 Porto, Portugal"},{"name":"Department of Design in Engineering, University of Vigo, 36312 Vigo, Spain"},{"name":"Design, Expert Systems and Artificial Intelligent Solutions Group (DESAINS), Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36312 Vigo, Spain"},{"name":"Center for Health Technologies and Information Systems Research\u2014CINTESIS@RISE, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105162","DOI":"10.1016\/j.jstrokecerebrovasdis.2020.105162","article-title":"Machine learning for brain stroke: A review","volume":"29","author":"Sirsat","year":"2020","journal-title":"J. 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