He Haoying, Lu Dongwei, Peng Sisi, Jiang Jiu, Fan Fan, Sun Dong, Sun Tianqi, Xu Zhipeng, Zhang Ping, Peng Xiaoxiang, Lei Ming, Zhang Junjian
Department of Neurology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Department of Neuropsychology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Front Neurol. 2025 May 29;16:1505739. doi: 10.3389/fneur.2025.1505739. eCollection 2025.
Vascular cognitive impairment (VCI) is prevalent but underdiagnosed due to its heterogeneous nature and the lack of reliable diagnostic tools. Machine learning (ML) enhances disease evaluation by enabling accurate prediction and early detection from complex data. This study aimed to develop ML models to detect VCI using clinical data and multimodal MRI, and to explore the associations between imaging markers and cognitive function.
The study enrolled 313 participants from Wuhan and surrounding areas, including 157 patients with VCI (age 62.38 ± 6.62 years, education 10.83 ± 3.00 years) and 156 cognitively normal individuals with vascular risk factors (age 59.93 ± 6.74 years, education 13.97 ± 3.19 years). An independent dataset of 82 participants was used for external validation. Clinical data, neuropsychological assessments, and MRIs (T1, T2-FLAIR, and DTI) were collected. After imaging processing and preliminary model selection, optimal models using various data modalities were constructed. Model reduction was undertaken to simplify models without sacrificing performance. SHapley Additive exPlanations and moDel Agnostic Language for Exploration and eXplanation were used for model interpretation.
The comprehensive final model integrating clinical and multimodal MRI measures achieved the best performance with eight input variables (AUC of 0.956, 95%CI 0.919-0.988 for internal and 0.919, 95%CI 0.866-0.966 for external validation). During external validation, DTI demonstrated more stable performance than T1 and T2-FLAIR imaging, highlighting its potential importance over conventional imaging markers. Key imaging markers, especially along the lateral cholinergic pathway, were highlighted for their importance in diagnosing VCI and understanding its manifestation.
Our study developed and validated accurate ML models for VCI detection, emphasizing the importance of DTI. The identified imaging markers, particularly those derived from DTI, underscoring the potential in enhancing diagnostic accuracy and understanding cognitive impairments related to vascular changes.
血管性认知障碍（VCI）很常见，但由于其异质性和缺乏可靠的诊断工具，目前诊断不足。机器学习（ML）通过对复杂数据进行准确预测和早期检测，增强了疾病评估能力。本研究旨在开发利用临床数据和多模态磁共振成像（MRI）检测VCI的ML模型，并探索影像标志物与认知功能之间的关联。
本研究招募了来自武汉及周边地区的313名参与者，包括157例VCI患者（年龄62.38±6.62岁，受教育年限10.83±3.00年）和156名有血管危险因素的认知正常个体（年龄59.93±6.74岁，受教育年限13.97±3.19年）。使用一个包含82名参与者的独立数据集进行外部验证。收集了临床数据、神经心理学评估结果和MRI（T1、T2-FLAIR和DTI）数据。在进行影像处理和初步模型选择后，构建了使用各种数据模式的最优模型。进行模型简化以在不牺牲性能的情况下简化模型。使用SHapley加性解释和探索与解释的模型无关语言进行模型解释。
整合临床和多模态MRI测量的综合最终模型在八个输入变量下表现最佳（内部验证的AUC为0.956，95%CI为0.919-0.988；外部验证的AUC为0.919，95%CI为0.866-0.966）。在外部验证期间，DTI表现出比T1和T2-FLAIR成像更稳定的性能，突出了其相对于传统影像标志物的潜在重要性。关键影像标志物，特别是沿外侧胆碱能通路的标志物，因其在诊断VCI和理解其表现方面的重要性而受到关注。
我们研究开发并验证了用于VCI检测的准确ML模型，强调了DTI的重要性。所确定的影像标志物，特别是那些来自DTI的标志物，突出了其在提高诊断准确性和理解与血管变化相关的认知障碍方面的潜力。
