1. Artificial intelligence in ischemic stroke images: current applications and future directions
- Author
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Ying Liu, Zhongjian Wen, Yiren Wang, Yuxin Zhong, Jianxiong Wang, Yiheng Hu, Ping Zhou, and Shengmin Guo
- Subjects
ischemic stroke ,medical imaging ,deep learning ,machine learning ,artificial intelligence ,prediction model ,Neurology. Diseases of the nervous system ,RC346-429 - Abstract
This paper reviews the current research progress in the application of Artificial Intelligence (AI) based on ischemic stroke imaging, analyzes the main challenges, and explores future research directions. This study emphasizes the application of AI in areas such as automatic segmentation of infarct areas, detection of large vessel occlusion, prediction of stroke outcomes, assessment of hemorrhagic transformation risk, forecasting of recurrent ischemic stroke risk, and automatic grading of collateral circulation. The research indicates that Machine Learning (ML) and Deep Learning (DL) technologies have tremendous potential for improving diagnostic accuracy, accelerating disease identification, and predicting disease progression and treatment responses. However, the clinical application of these technologies still faces challenges such as limitations in data volume, model interpretability, and the need for real-time monitoring and updating. Additionally, this paper discusses the prospects of applying large language models, such as the transformer architecture, in ischemic stroke imaging analysis, emphasizing the importance of establishing large public databases and the need for future research to focus on the interpretability of algorithms and the comprehensiveness of clinical decision support. Overall, AI has significant application value in the management of ischemic stroke; however, existing technological and practical challenges must be overcome to achieve its widespread application in clinical practice.
- Published
- 2024
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