Machine learning studies on major brain diseases: 5-year trends of 2014-2018.
In the recent 5 years (2014-2018), there has been growing interest in the use of machine learning (ML) techniques to explore image diagnosis and prognosis of therapeutic lesion changes within the area of neuroradiology. However, to date, the majority of research trend and current status have not bee...
| Publicado en: | Japanese Journal of Radiology Vol. 37; no. 1; pp. 34 - 73 |
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| Autores principales: | , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=133940281&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133940281 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 18671071 AUCM jtl: Japanese Journal of Radiology issn: 18671071 maglogo: N pubinfo: dt: Jan2019 vid: 37 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 133940281 133940281 NLM30498877 133940281 10.1007/s11604-018-0794-4 NLM30498877 133940281 ppf: 34 ppct: 39 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning studies on major brain diseases: 5-year trends of 2014-2018. aug: au: Sakai, Koji Yamada, Kei affil: Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kajii-cho, Kawaramachi Hirokoji Agaru, Kamigyo-ku, 602-8566, Kyoto, Kyoto, Japan sug: subj: Neuroradiography Methods Brain Brain Diseases Brain Pathology Human Brain Diseases Pathology Systematic Review ab: In the recent 5 years (2014-2018), there has been growing interest in the use of machine learning (ML) techniques to explore image diagnosis and prognosis of therapeutic lesion changes within the area of neuroradiology. However, to date, the majority of research trend and current status have not been clearly illuminated in the neuroradiology field. More than 1000 papers have been published during the past 5 years on subject classification and prediction focused on multiple brain disorders. We provide a survey of 209 papers in this field with a focus on top ten active areas of research; i.e., Alzheimer's disease/mild cognitive impairment, brain tumor; schizophrenia, depressive disorders, Parkinson's disease, attention-deficit hyperactivity disorder, autism spectrum disease, epilepsy, multiple sclerosis, stroke, and traumatic brain injury. Detailed information of these studies, such as ML methods, sample size, type of inputted features and reported accuracy, are summarized. This paper reviews the evidences, current limitations and status of studies using ML to assess brain disorders in neuroimaging data. The main bottleneck of this research field is still the limited sample size, which could be potentially addressed by modern data sharing models, such as ADNI. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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