[Progress in computer-assisted Alberta stroke program early computer tomography score of acute ischemic stroke based on different modal images].
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
; 38(4): 790-796, 2021 Aug 25.
Article
em Zh
| MEDLINE
| ID: mdl-34459180
ABSTRACT
Clinically, non-contrastive computed tomography (NCCT) is used to quickly diagnose the type and area of ââstroke, and the Alberta stroke program early computer tomography score (ASPECTS) is used to guide the next treatment. However, in the early stage of acute ischemic stroke (AIS), it's difficult to distinguish the mild cerebral infarction on NCCT with the naked eye, and there is no obvious boundary between brain regions, which makes clinical ASPECTS difficult to conduct. The method based on machine learning and deep learning can help physicians quickly and accurately identify cerebral infarction areas, segment brain areas, and operate ASPECTS quantitative scoring, which is of great significance for improving the inconsistency in clinical ASPECTS. This article describes current challenges in the field of AIS ASPECTS, and then summarizes the application of computer-aided technology in ASPECTS from two aspects including machine learning and deep learning. Finally, this article summarizes and prospects the research direction of AIS-assisted assessment, and proposes that the computer-aided system based on multi-modal images is of great value to improve the comprehensiveness and accuracy of AIS assessment, which has the potential to open up a new research field for AIS-assisted assessment.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Isquemia Encefálica
/
Acidente Vascular Cerebral
/
AVC Isquêmico
Tipo de estudo:
Prognostic_studies
Limite:
Humans
País/Região como assunto:
America do norte
Idioma:
Zh
Revista:
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
Assunto da revista:
ENGENHARIA BIOMEDICA
Ano de publicação:
2021
Tipo de documento:
Article