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Alberta Stroke Program Early CT Score Calculation Using the Deep Learning-Based Brain Hemisphere Comparison Algorithm
The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a promising tool for the evaluation of stroke expansion to determine suitability for reperfusion therapy. The aim of this study was to validate deep learning-based ASPECTS calculation software that utilizes a three-dimensional fully convolutional network-based brain hemisphere comparison algorithm (3D-BHCA).
Source: Journal of Stroke and Cerebrovascular Diseases - April 17, 2021 Category: Neurology Authors: Masaki Naganuma, Atsushi Tachibana, Takuya Fuchigami, Sadato Akahori, Shuichiro Okumura, Kenichiro Yi, Yoshimasa Matsuo, Koichi Ikeno, Toshiro Yonehara Source Type: research

End-to-end artificial intelligence platform for the management of large vessel occlusions: A preliminary study
In this study, we developed a deep learning pipeline that detects large vessel occlusion (LVO) and predicts functional outcome based on computed tomography angiography (CTA) images to improve the management of the LVO patients.
Source: Journal of Stroke and Cerebrovascular Diseases - September 15, 2022 Category: Neurology Authors: Shujuan Meng, Thi My Linh Tran, Mingzhe Hu, PanPan Wang, Thomas Yi, Zhusi Zhong, Luoyun Wang, Braden Vogt, Zhicheng Jiao, Arko Barman, Ugur Cetintemel, Ken Chang, Dat-Thanh Nguyen, Ferdinand K. Hui, Ian Pan, Bo Xiao, Li Yang, Hao Zhou, Harrison X. Bai Source Type: research

Machine learning-based identification of symptomatic carotid atherosclerotic plaques with dual-energy computed tomography angiography
This study aimed to develop and validate a machine learning model incorporating both dual-energy computed tomography (DECT) angiography quantitative parameters and clinically relevant risk factors for the identification of symptomatic carotid plaques to prevent acute cerebrovascular events.
Source: Journal of Stroke and Cerebrovascular Diseases - June 7, 2023 Category: Neurology Authors: Ling-Jie Wang, Pei-Qing Zhai, Li-Li Xue, Cai-Yun Shi, Qian Zhang, Hua Zhang Source Type: research