Detection of Alzheimer's Disease Using Hybrid Meta-ROI of MRI Structural Images.

Xiaoming Zheng, On Behalf Of The Alzheimer's Disease Neuroimaging Initiative

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Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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2 authors.

Xiaoming ZhengSchool of Dentistry and Medical Sciences and Rural Health Research Institute, Charles Sturt University, Wagga Wagga, NSW 2678, Australia.ORCID 0000-0003-0918-2624

On Behalf Of The Alzheimer's Disease Neuroimaging InitiativeSchool of Dentistry and Medical Sciences and Rural Health Research Institute, Charles Sturt University, Wagga Wagga, NSW 2678, Australia.

Funding

Rural Health Research Institute, Charles Sturt University. N/A

6 · The paper itself

Abstract

backgroundThe averaged cortical thickness of meta-ROI is currently being used for the diagnosis and prognosis of Alzheimer's disease (AD) using structural MRI brain images. The purpose of this work is to present a hybrid meta-ROI for the detection of AD.

methodsThe AD detectability of selected cortical and volumetric regions of the brain was examined using signal detection theory. The top performing cortical and volumetric ROIs were taken as input nodes to the artificial neural network (ANN) for AD classification.

resultsAn AD diagnostic accuracy of 91.9% was achieved by using a hybrid meta-ROI consisting of thicknesses of entorhinal and middle temporal cortices, and the volumes of the hippocampus and inferior lateral ventricles. Pairing inferior lateral ventricle dilation with hippocampal volume reduction improves AD detectability by 5.1%.

conclusionsHybrid meta-ROI, including the dilation of inferior lateral ventricles, outperformed both cortical thickness- and volumetric-based meta-ROIs in the detection of Alzheimer's disease.

Indexed as

Alzheimer’s diseaseartificial neural networkMeta-ROIsignal detection theorystructural MRI images

Identifiers

PMID39410607

DOI10.3390/diagnostics14192203

PMCPMC11475774

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.