基于mri的轻度认知障碍和阿尔茨海默病分类,采用变分自编码器和其他机器学习分类器相结合的算法。

IF 2.8 Q2 NEUROSCIENCES
Journal of Alzheimer's disease reports Pub Date : 2024-10-18 eCollection Date: 2024-01-01 DOI:10.1177/25424823241290694
Subhrangshu Bit, Pritam Dey, Arnab Maji, Tapan K Khan
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引用次数: 0

摘要

背景:正确诊断轻度认知障碍(MCI)和阿尔茨海默病(AD)对药物研发中的患者选择具有重要意义。利用神经影像结合脑脊液和遗传生物标志物进行分期诊断的研究成果既昂贵又耗时。仅使用来自两个国际公认数据集的结构磁共振成像(sMRI)扫描作为输入,以及测试和独立验证,以确定机器学习算法对痴呆症的分类。目的:使用变分自编码器(VAE)从sMRI扫描中提取降维潜在特征向量。目的是在没有任何其他信息的情况下,利用MRI对AD、MCI和对照(CN)进行分类。方法:将VAE提取的MRI扫描特征向量作为不同高级机器学习分类器的输入条件。在两个队列中,使用MRI图像的VAE输出和不同的人工智能/机器学习分类器模型对AD/CN/MCI进行分类。结果:仅使用MRI扫描,研究的主要目的是测试从CN和MCI病例中区分AD的能力。本研究在测试集中,AD对CN的分类准确率为75.45% (F1-score = 79.52%), AD对MCI的分类准确率为81.41% (F1-score = 87.06%),尸检证实的AD对MCI的分类准确率为92.75% (F1-score = 95.52%),验证数据集中AD对CN的分类准确率为86.16% (F1-score = 92.03%), AD对MCI的分类准确率为70.03% (F1-score = 82.1%)。结论:通过克服数据泄露问题,在两个独立的队列中对尸检确认的机器学习分类模型进行了测试。独立队列的外部验证提高了分类算法的质量和新颖性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MRI-based mild cognitive impairment and Alzheimer's disease classification using an algorithm of combination of variational autoencoder and other machine learning classifiers.

Background: Correctly diagnosing mild cognitive impairment (MCI) and Alzheimer's disease (AD) is important for patient selection in drug discovery. Research outcomes on stage diagnosis using neuroimages combined with cerebrospinal fluid and genetic biomarkers are expensive and time-consuming. Only structural magnetic resonance imaging (sMRI) scans from two internationally recognized datasets are employed as input as well as test and independent validation to determine the classification of dementia by the machine learning algorithm.

Objective: We extract the reduced dimensional latent feature vector from the sMRI scans using a variational autoencoder (VAE). The objective is to classify AD, MCI, and control (CN) using MRI and without any other information.

Methods: The extracted feature vectors from MRI scans by VAE are used as input conditions for different advanced machine-learning classifiers. Classification of AD/CN/MCI are conducted using the output of VAE from MRI images and different artificial intelligence/machine learning classifier models in two cohorts.

Results: Using only MRI scans, the primary goal of the study is to test the ability to classify AD from CN and MCI cases. The current study achieved classification accuracies of AD versus CN 75.45% (F1-score = 79.52%), AD versus MCI 81.41% (F1-Score = 87.06%), and autopsy-confirmed AD versus MCI 92.75% (F1-Score = 95.52%) in test sets and AD versus CN 86.16% (F1-score = 92.03%) and AD versus MCI 70.03% (F1-Score = 82.1%) in validation data set.

Conclusions: By overcoming the data leakage problem, the autopsy-confirmed machine learning classification model is tested in two independent cohorts. External validation by an independent cohort improved the quality and novelty of the classification algorithm.

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