Fei Beini, Cheng Yu, Liu Ying, Zhang Guangzheng, Ge Anyan, Luo Junyi, Wu Shan, Wang He, Ding Jing, Wang Xin
Department of Neurology, Zhongshan Hospital Fudan University, Shanghai, China.
Fudan University Institute of Science and Technology for Brain-inspired Intelligence, Shanghai, China.
Stroke Vasc Neurol. 2024 Dec 30;9(6):699-707. doi: 10.1136/svn-2023-002976.
The injury of the cholinergic white matter pathway underlies cognition decline in patients with silent cerebrovascular disease (SCD) with white matter hyperintensities (WMH) of vascular origin. However, the evaluation of the cholinergic white matter pathway is complex with poor consistency. We established an intelligent algorithm to evaluate WMH in the cholinergic pathway.
Patients with SCD with WMH of vascular origin were enrolled. The Cholinergic Pathways Hyperintensities Scale (CHIPS) was used to measure cholinergic white matter pathway impairment. The intelligent algorithm used a deep learning model based on convolutional neural networks to achieve WMH segmentation and CHIPS scoring. The diagnostic value of the intelligent algorithm for moderate-to-severe cholinergic pathway injury was calculated. The correlation between the WMH in the cholinergic pathway and cognitive function was analysed.
A number of 464 patients with SCD were enrolled in internal training and test set. The algorithm was validated using data from an external cohort comprising 100 patients with SCD. The sensitivity, specificity and area under the curve of the intelligent algorithm to assess moderate and severe cholinergic white matter pathway injury were 91.7%, 87.3%, 0.903 (95% CI 0.861 to 0.952) and 86.5%, 81.3%, 0.868 (95% CI 0.819 to 0.921) for the internal test set and external validation set. for the. The general cognitive function, execution function and attention showed significant differences among the three groups of different CHIPS score (all p<0.05).
We have established the first intelligent algorithm to evaluate the cholinergic white matter pathway with good accuracy compared with the gold standard. It helps more easily assess the cognitive function in patients with SCD.
在伴有血管源性白质高信号（WMH）的无症状脑血管疾病（SCD）患者中，胆碱能白质通路损伤是认知功能下降的基础。然而，胆碱能白质通路的评估复杂且一致性差。我们建立了一种智能算法来评估胆碱能通路中的WMH。
纳入伴有血管源性WMH的SCD患者。使用胆碱能通路高信号量表（CHIPS）来测量胆碱能白质通路损伤。该智能算法使用基于卷积神经网络的深度学习模型来实现WMH分割和CHIPS评分。计算该智能算法对中重度胆碱能通路损伤的诊断价值。分析胆碱能通路中的WMH与认知功能之间的相关性。
464例SCD患者被纳入内部训练和测试集。该算法使用来自100例SCD患者的外部队列数据进行验证。内部测试集和外部验证集评估中重度胆碱能白质通路损伤的智能算法的敏感性、特异性和曲线下面积分别为91.7%、87.3%、0.903（95%CI 0.861至0.952）和86.5%、81.3%、0.868（95%CI 0.819至0.921）。不同CHIPS评分的三组之间的总体认知功能、执行功能和注意力存在显著差异（均p<0.05）。
我们建立了首个评估胆碱能白质通路的智能算法，与金标准相比具有良好的准确性。它有助于更轻松地评估SCD患者的认知功能。
