Xu Dong, Li Hao, Su Fanghui, Qiu Sizheng, Tong Huixia, Huang Meifeng, Yao Jianzhong
Department of Neuroelectrophysiology, Anyang People's Hospital, Anyang, China.
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China.
Front Neurol. 2024 Oct 16;15:1394435. doi: 10.3389/fneur.2024.1394435. eCollection 2024.
The diagnosis of intracranial atherosclerotic stenosis (ICAS) is of great significance for the prevention of stroke. Deep learning (DL)-based artificial intelligence techniques may aid in the diagnosis. The study aimed to identify ICAS in the middle cerebral artery (MCA) based on a modified DL model.
This retrospective study included two datasets. Dataset1 consisted of 3,068 transcranial Doppler (TCD) images of the MCA from 1,729 patients, which were assessed as normal or stenosis by three physicians with varying levels of experience, in conjunction with other medical imaging data. The data were used to improve and train the VGG16 models. Dataset2 consisted of TCD images of 90 people who underwent physical examination, which were used to verify the robustness of the model and compare the consistency between the model and human physicians.
The accuracy, precision, specificity, sensitivity, and area under curve (AUC) of the best model VGG16 + Squeeze-and-Excitation (SE) + skip connection (SC) on dataset1 reached 85.67 ± 0.43(%),87.23 ± 1.17(%),87.73 ± 1.47(%),83.60 ± 1.60(%), and 0.857 ± 0.004, while those of dataset2 were 93.70 ± 2.80(%),62.65 ± 11.27(%),93.00 ± 3.11(%),100.00 ± 0.00(%), and 0.965 ± 0.016. The kappa coefficient showed that it reached the recognition level of senior doctors.
The improved DL model has a good diagnostic effect for MCV stenosis in TCD images and is expected to help in ICAS screening.
颅内动脉粥样硬化性狭窄（ICAS）的诊断对于预防中风具有重要意义。基于深度学习（DL）的人工智能技术可能有助于诊断。本研究旨在基于改进的DL模型识别大脑中动脉（MCA）中的ICAS。
这项回顾性研究包括两个数据集。数据集1由来自1729名患者的3068张MCA的经颅多普勒（TCD）图像组成，由三名经验水平不同的医生结合其他医学影像数据将其评估为正常或狭窄。这些数据用于改进和训练VGG16模型。数据集2由90名接受体检者的TCD图像组成，用于验证模型的稳健性并比较模型与人类医生之间的一致性。
最佳模型VGG16+挤压激励（SE）+跳跃连接（SC）在数据集1上的准确率、精确率、特异性、敏感性和曲线下面积（AUC）分别达到85.67±0.43（%）、87.23±1.17（%）、87.73±1.47（%）、83.60±1.60（%）和0.857±0.004，而在数据集2上分别为93.70±2.80（%）、62.65±11.27（%）、93.00±3.11（%）、100.00±0.00（%）和0.965±0.016。kappa系数表明其达到了高级医生的识别水平。
改进后的DL模型对TCD图像中的MCV狭窄具有良好的诊断效果，有望有助于ICAS筛查。
