Suhavi Kaur Bhatia, S. Goyal, Tripatjot Singh Arora, Rishu Chhabra
{"title":"Role of Artificial Intelligence in Brain Stroke Management: A survey","authors":"Suhavi Kaur Bhatia, S. Goyal, Tripatjot Singh Arora, Rishu Chhabra","doi":"10.1109/DELCON57910.2023.10127366","DOIUrl":null,"url":null,"abstract":"Deep Learning (DL) and Machine Learning (ML) are the key subsets of Artificial Intelligence that have evolved into an important tool in healthcare settings. Brain stroke management is one of the applications where these computer based techniques can help the patients with better diagnosis and individualized clinical care. However, stroke diagnosis and prognosis are dependent on a number of clinical and individual factors. To increase diagnostic and prognostic accuracy, the development of efficient ML and DL algorithms and thorough data collection and assimilation is the key. In this paper, we present a survey of deep learning and machine learning techniques for brain stroke management. The techniques have been categorized on the basis of type of cerebral stroke: ischemic stroke and hemorrhagic stroke. The paper concludes with the future research directions in the area of brain stroke management.","PeriodicalId":193577,"journal":{"name":"2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DELCON57910.2023.10127366","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Deep Learning (DL) and Machine Learning (ML) are the key subsets of Artificial Intelligence that have evolved into an important tool in healthcare settings. Brain stroke management is one of the applications where these computer based techniques can help the patients with better diagnosis and individualized clinical care. However, stroke diagnosis and prognosis are dependent on a number of clinical and individual factors. To increase diagnostic and prognostic accuracy, the development of efficient ML and DL algorithms and thorough data collection and assimilation is the key. In this paper, we present a survey of deep learning and machine learning techniques for brain stroke management. The techniques have been categorized on the basis of type of cerebral stroke: ischemic stroke and hemorrhagic stroke. The paper concludes with the future research directions in the area of brain stroke management.