Deep learning-assisted diagnosis of large vessel occlusion in acute ischemic stroke based on four-dimensional computed tomography angiography
作者全名:Peng, Yuling; Liu, Jiayang; Yao, Rui; Wu, Jiajing; Li, Jing; Dai, Linquan; Gu, Sirun; Yao, Yunzhuo; Li, Yongmei; Chen, Shanxiong; Wang, Jingjie
作者地址:[Peng, Yuling; Liu, Jiayang; Wu, Jiajing; Li, Jing; Dai, Linquan; Gu, Sirun; Yao, Yunzhuo; Li, Yongmei; Wang, Jingjie] Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing, Peoples R China; [Yao, Rui; Chen, Shanxiong] Southwest Univ, Coll Comp & Informat Sci, Chongqing, Peoples R China
通信作者:Wang, JJ (通讯作者),Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing, Peoples R China.; Chen, SX (通讯作者),Southwest Univ, Coll Comp & Informat Sci, Chongqing, Peoples R China.
来源:FRONTIERS IN NEUROSCIENCE
ESI学科分类:NEUROSCIENCE & BEHAVIOR
WOS号:WOS:001206717900001
JCR分区:Q2
影响因子:3.2
年份:2024
卷号:18
期号:
开始页:
结束页:
文献类型:Article
关键词:large vessel occlusion; stroke; deep learning; four-dimensional computed tomography angiography; artificial neural network
摘要:Purpose To develop deep learning models based on four-dimensional computed tomography angiography (4D-CTA) images for automatic detection of large vessel occlusion (LVO) in the anterior circulation that cause acute ischemic stroke.Methods This retrospective study included 104 LVO patients and 105 non-LVO patients for deep learning models development. Another 30 LVO patients and 31 non-LVO patients formed the time-independent validation set. Four phases of 4D-CTA (arterial phase P1, arterial-venous phase P2, venous phase P3 and late venous phase P4) were arranged and combined and two input methods was used: combined input and superimposed input. Totally 26 models were constructed using a modified HRNet network. Assessment metrics included the areas under the curve (AUC), accuracy, sensitivity, specificity and F1 score. Kappa analysis was performed to assess inter-rater agreement between the best model and radiologists of different seniority.Results The P1 + P2 model (combined input) had the best diagnostic performance. In the internal validation set, the AUC was 0.975 (95%CI: 0.878-0.999), accuracy was 0.911, sensitivity was 0.889, specificity was 0.944, and the F1 score was 0.909. In the time-independent validation set, the model demonstrated consistently high performance with an AUC of 0.942 (95%CI: 0.851-0.986), accuracy of 0.902, sensitivity of 0.867, specificity of 0.935, and an F1 score of 0.901. The best model showed strong consistency with the diagnostic efficacy of three radiologists of different seniority (k = 0.84, 0.80, 0.70, respectively).Conclusion The deep learning model, using combined arterial and arterial-venous phase, was highly effective in detecting LVO, alerting radiologists to speed up the diagnosis.
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