Dataset Information

Odor Identification Screening Improves Diagnostic Classification in Incipient Alzheimer's Disease.

ABSTRACT:

Background

Measurements of olfaction may serve as useful biomarkers of incipient dementia. Here we examine the improvement in diagnostic accuracy of Alzheimer's disease (AD) and mild cognitive impairment (MCI) when assessing both cognitive functioning and odor identification.

Objective

To determine the utility of odor identification as a supplementary screening test in incipient AD.

Methods

Sniffin' Sticks Odor Identification Test (SS-OIT) and the Montreal Cognitive Assessment (MoCA) were administered in 262 AD, 174 MCI [150 amnestic (aMCI), and 24 non-amnestic (naMCI)], and 292 healthy older adults (HOA).

Results

Odor identification scores were higher in HOA relative to MCI or AD groups, and MCI outperformed AD. Odor identification scores were higher in aMCI single domain than aMCI multiple domain. Complementing MoCA scores with the SS-OIT significantly improved diagnostic accuracy of individuals with AD and MCI, including within MCI subgroups.

Discussion

Odor identification is a useful supplementary screening tool that provides additional information relevant for clinical categorization of AD and MCI, including those who are at highest risk to convert to AD.

SUBMITTER: Quarmley M

PROVIDER: S-EPMC5350629 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Odor Identification Screening Improves Diagnostic Classification in Incipient Alzheimer's Disease.

Quarmley Megan M Moberg Paul J PJ Mechanic-Hamilton Dawn D Kabadi Sushila S Arnold Steven E SE Wolk David A DA Roalf David R DR

Journal of Alzheimer's disease : JAD 20170101 4

<h4>Background</h4>Measurements of olfaction may serve as useful biomarkers of incipient dementia. Here we examine the improvement in diagnostic accuracy of Alzheimer's disease (AD) and mild cognitive impairment (MCI) when assessing both cognitive functioning and odor identification.<h4>Objective</h4>To determine the utility of odor identification as a supplementary screening test in incipient AD.<h4>Methods</h4>Sniffin' Sticks Odor Identification Test (SS-OIT) and the Montreal Cognitive Assessm ...[more]

PMID: 27886011

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Project description:Deep learning, a state-of-the-art machine learning approach, has shown outstanding performance over traditional machine learning in identifying intricate structures in complex high-dimensional data, especially in the domain of computer vision. The application of deep learning to early detection and automated classification of Alzheimer's disease (AD) has recently gained considerable attention, as rapid progress in neuroimaging techniques has generated large-scale multimodal neuroimaging data. A systematic review of publications using deep learning approaches and neuroimaging data for diagnostic classification of AD was performed. A PubMed and Google Scholar search was used to identify deep learning papers on AD published between January 2013 and July 2018. These papers were reviewed, evaluated, and classified by algorithm and neuroimaging type, and the findings were summarized. Of 16 studies meeting full inclusion criteria, 4 used a combination of deep learning and traditional machine learning approaches, and 12 used only deep learning approaches. The combination of traditional machine learning for classification and stacked auto-encoder (SAE) for feature selection produced accuracies of up to 98.8% for AD classification and 83.7% for prediction of conversion from mild cognitive impairment (MCI), a prodromal stage of AD, to AD. Deep learning approaches, such as convolutional neural network (CNN) or recurrent neural network (RNN), that use neuroimaging data without pre-processing for feature selection have yielded accuracies of up to 96.0% for AD classification and 84.2% for MCI conversion prediction. The best classification performance was obtained when multimodal neuroimaging and fluid biomarkers were combined. Deep learning approaches continue to improve in performance and appear to hold promise for diagnostic classification of AD using multimodal neuroimaging data. AD research that uses deep learning is still evolving, improving performance by incorporating additional hybrid data types, such as-omics data, increasing transparency with explainable approaches that add knowledge of specific disease-related features and mechanisms.

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Dataset Information

Odor Identification Screening Improves Diagnostic Classification in Incipient Alzheimer's Disease.

Background

Objective

Methods

Results

Discussion

Publications

Odor Identification Screening Improves Diagnostic Classification in Incipient Alzheimer's Disease.

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