{Reference Type}: English Abstract
{Title}: [An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion].
{Author}: Zeng A;Wang J;Pan D;Yang Y;Liu J;Liu X;Chen W;Wu J;
{Journal}: Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
{Volume}: 41
{Issue}: 3
{Year}: 2024 Jun 25
暂无{DOI}: 10.7507/1001-5515.202310046
{Abstract}: Alzheimer's Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is challenging, leading to a high rate of misdiagnosis. Current research on early diagnosis of AD has not sufficiently focused on tracking the progression of the disease over an extended period in subjects. To address this issue, this paper proposes an ensemble model for assisting early diagnosis of AD that combines structural magnetic resonance imaging (sMRI) data from two time points with clinical information. The model employs a three-dimensional convolutional neural network (3DCNN) and twin neural network modules to extract features from the sMRI data of subjects at two time points, while a multi-layer perceptron (MLP) is used to model the clinical information of the subjects. The objective is to extract AD-related features from the multi-modal data of the subjects as much as possible, thereby enhancing the diagnostic performance of the ensemble model. Experimental results show that based on this model, the classification accuracy rate is 89% for differentiating AD patients from normal controls (NC), 88% for differentiating mild cognitive impairment converting to AD (MCIc) from NC, and 69% for distinguishing non-converting mild cognitive impairment (MCInc) from MCIc, confirming the effectiveness and efficiency of the proposed method for early diagnosis of AD, as well as its potential to play a supportive role in the clinical diagnosis of early Alzheimer's disease.<BR>阿尔茨海默症（AD）是一种进行性神经退行性疾病。由于AD患者早期阶段的病症不明显，使得临床诊断中难以快速确诊，误诊率较高。目前关于AD早期诊断的相关研究中，较少关注受试者较长时间跨度上AD的进展变化。基于此，本文提出融合双时间点结构性磁共振成像（sMRI）的AD早期辅助诊断集成模型，尝试将受试者在两个时间点上获取的sMRI变化和临床信息纳入到预测模型的分析中，并使用三维卷积神经网络（3DCNN）和孪生神经网络模块从受试者两个时间点的sMRI中进行特征提取，同时用多层感知机（MLP）来对受试者的临床信息进行建模，尽可能从受试者的多模态数据中提取与AD相关的特征，提高集成模型的诊断性能。实验结果表明，基于本文模型，AD患者组与正常对照（NC）组的分类准确率为89%，转化为AD的轻度认知障碍（MCIc）组与NC组的分类准确率达88%，不转化为AD的轻度认知障碍（MCInc）组与MCIc组的分类准确率为69%，证实了本文所提方法在AD早期诊断中的有效性和高效性，有望在AD早期的临床诊断中起辅助支持的作用。.