Progress in computer-assisted Alberta stroke program early computer tomography score of acute ischemic stroke based on different modal images.
10.7507/1001-5515.202012037
- Author:
Naijia LIU
1
;
Ying HU
1
;
Yifeng YANG
1
;
Yuehua LI
2
;
Shengdong NIE
1
Author Information
1. Institute of Medical Imaging Engineering, University of Shanghai for Science & Technology, Shanghai 200093, P.R.China.
2. Department of Radiology Intervention, Sixth People's Hospital Affiliated to Shanghai Jiaotong University, Shanghai 200233, P.R. China.
- Publication Type:Journal Article
- Keywords:
Alberta stroke program early computer tomography score;
acute ischemic stroke;
computer-aided diagnosis;
deep learning
- MeSH:
Alberta;
Brain Ischemia/diagnostic imaging*;
Humans;
Ischemic Stroke;
Stroke/diagnostic imaging*;
Tomography, X-Ray Computed
- From:
Journal of Biomedical Engineering
2021;38(4):790-796
- CountryChina
- Language:Chinese
-
Abstract:
Clinically, non-contrastive computed tomography (NCCT) is used to quickly diagnose the type and area of stroke, 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.
