AI Article Synopsis
- A hybrid meta-region of interest (meta-ROI) was developed to enhance detection of Alzheimer’s disease (AD) using structural MRI brain imaging.
- The study identified that combining cortical thickness of specific brain areas with volumetric measurements significantly improved AD classification accuracy to 91.9%.
- Incorporating the dilation of the inferior lateral ventricles alongside hippocampal volume provided a 5.1% better detection rate compared to traditional methods.
Article Abstract
Background: The 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.
Methods: The 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.
Results: An 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%.
Conclusions: Hybrid 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.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11475774 | PMC |
| http://dx.doi.org/10.3390/diagnostics14192203 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!