English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Alzheimer's Disease (AD) is primarily an irreversible neurodegenerativedisorder affecting millions of individuals today. The prognosis of the diseasesolely depends on treating symptoms as they arise and proper caregiving, asthere are no current medical preventative treatments. For this purpose, earlydetection of the disease at its most premature state is of paramountimportance. This work aims to survey imaging biomarkers corresponding to theprogression of Alzheimer's Disease (AD). A longitudinal study of structural MRimages was performed for given temporal test subjects selected randomly fromthe Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The pipelineimplemented includes modern pre-processing techniques such as spatial imageregistration, skull stripping, and inhomogeneity correction. The temporal dataacross multiple visits spanning several years helped identify the structuralchange in the form of volumes of cerebrospinal fluid (CSF), grey matter (GM),and white matter (WM) as the patients progressed further into the disease.Tissue classes are segmented using an unsupervised learning approach usingintensity histogram information. The segmented features thus extracted provideinsights such as atrophy, increase or intolerable shifting of GM, WM and CSFand should help in future research for automated analysis of Alzheimer'sdetection with clinical domain explainability.

Longitudinal Volumetric Study for the Progression of Alzheimer's Disease from Structural MR Images