Jiaxi Lu ¹ Mazen Yassin ¹ Yingwei Guo ² Yang Yingjian ³ Fengqiu Cao ^4* Jiajing Fang ^5* Asim Zaman ¹ HASSAN HASEEB ⁶ Xueqiang Zeng ¹ Xiaoqiang Miao ^7* Huihui Yang ¹ Anbo Cao ¹ Guangtao Huang ^1* Taiyu Han ^1* Yu Luo ^8* Yan Kang ^6*

• ¹ Shenzhen University, Shenzhen, China
• ² Northeast Petroleum University, Daqing, China
• ³ Shenzhen Lanmage Medical Technology Co., Ltd, Shenzhen, China
• ⁴ Shenyang University of Technology, Shenyang, Liaoning Province, China
• ⁵ Shenzhen Academy of Metrology & Quality Inspection, Shenzhen, China
• ⁶ Shenzhen Technology University, Shenzhen, Guangdong, China
• ⁷ Northeastern University, Shenyang, Liaoning Province, China
• ⁸ Division of Spine Surgery, Department of Orthopedics, Tongji Hospital Affiliated to Tongji University School of Medicine, Shanghai, China

Accurate neurological impairment assessment is crucial for the clinical treatment and prognosis of patients with acute ischemic stroke (AIS). However, the original perfusion parameters lack the deep information for characterizing neurological impairment, leading to difficulty in accurate assessment. Given the advantages of radiomics technology in feature representation, this technology should provide more information for characterizing neurological impairment. Therefore, with its rigorous methodology, this study offers practical implications for clinical diagnosis by exploring the role of ischemic perfusion radiomics features in assessing the degree of neurological impairment. This study employs a meticulous methodology, starting with generating perfusion parameter maps through Dynamic Susceptibility Contrast-Perfusion Weighted Imaging (DSC-PWI) and determining ischemic regions based on these maps and a set threshold. Radiomics features are then extracted from the ischemic regions, and the t-test and least absolute shrinkage and selection operator (Lasso) algorithms are used to select the relevant features. Finally, the selected radiomics features and machine learning techniques are used to assess the degree of neurological impairment in AIS patients. The results show that the proposed method outperforms the original perfusion parameters, radiomics features of the infarct and hypoxic regions, and their combinations, achieving an accuracy of 0.926, sensitivity of 0.923, specificity of 0.929, PPV of 0.923, NPV of 0.929, and AUC of 0.923, respectively. Therefore, the proposed method effectively assesses the degree of neurological impairment in AIS patients, providing an objective auxiliary assessment tool for clinical diagnosis.