Study the combination of brain MRI imaging and other datatypes to improve Alzheimer's disease diagnosis
Study the combination of brain MRI imaging and other datatypes to improve Alzheimer's disease diagnosis
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研究脑部 MRI 成像与其他数据类型的结合，以改善阿尔茨海默病的诊断
DOI：
10.1101/2022.10.30.22281735
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
X. Huang
中科院分区：
文献类型：
--
作者：
J. Stubblefield;A. Kronberger;J. Causey;J. Qualls;J. Fowler;K. Zeng;K. Walker;X. Huang
阿尔茨海默氏病（AD）是一种退行性脑部疾病，是痴呆症的最常见原因。尽管 AD 是一种常见疾病，但人们对其知之甚少。目前 AD 的医学治疗旨在减缓疾病的进展。因此，早期发现AD对于在疾病的早期阶段进行干预非常重要。近年来，由于机器学习在计算机视觉领域取得的成功，利用机器学习预测算法进行的辅助临床诊断受到了极大的关注。在本研究中，我们结合了脑部MRI成像特征和其他数据类型的特征，并采用了各种模型，包括XGBoost、逻辑回归和k-最近邻，来改进AD诊断。我们在阿尔茨海默病神经影像计划的基准数据集上评估了模型。实验结果表明，逻辑回归模型在查准率、查全率和F1分数的评价指标上表现最好。该模型的预测可以为疑似阿尔茨海默病患者的诊断和预后提供有价值的信息。 XGBoost 模型实现了可比的性能，并有潜力成为疑似 AD 患者的有价值的诊断工具，通过重新发现先前已知的与 AD 的关联来进行自我验证。
Alzheimer's Disease (AD) is a degenerative brain disease and is the most common cause of dementia. Despite being a common disease, AD is poorly understood. Current medical treatments for AD are aimed at slowing the progression of the disease. So early detection of AD is important to intervene at an early stage of the disease. In recent years, by using machine learning predictive algorithms, assisted clinic diagnosis has received great attention due to its success of machine learning advances in the domains of computer vision. In this study, we have combined brain MRI imaging features and the features of other datatypes, and adopted various models, including XGBoost, logistic regression, and k-Nearest Neighbors, to improve AD diagnosis. We evaluated the models on the benchmark dataset of Alzheimer's Disease Neuroimaging Initiative. Experiment results show that the logistic regression model is the top performer in terms of evaluation metrics of precision, recall, and F1-score. The prediction of the models could provide valuable information for diagnosis and prognosis of patients with suspected Alzheimer's disease. The XGBoost model achieves a comparable performance and has the potential to serve as a valuable diagnostic tool for patients with suspected AD with its self-validation by re-discovering previously known associations with AD.
影响因子：
23.8
作者：
Lundberg, Scott M.;Erion, Gabriel;Lee, Su-In
通讯作者：
Lee, Su-In
影响因子：
19.7
作者：
Gatsonis, Constantine A.
通讯作者：
Gatsonis, Constantine A.