Artif Intell Med - Bilateral symmetry aspects in computer-aided Alzheimer's disease diagnosis by single-photon emission-computed tomography imaging.

Tópicos

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Resumo

JECTIVE: This paper explores the importance of the latent symmetry of the brain in computer-aided systems for diagnosing Alzheimer's disease (AD). Symmetry and asymmetry are studied from two points of view: (i) the development of an effective classifier within the scope of machine learning techniques, and (ii) the assessment of its relevance to the AD diagnosis in the early stages of the disease.METHODS: The proposed methodology is based on eigenimage decomposition of single-photon emission-computed tomography images, using an eigenspace extension to accommodate odd and even eigenvectors separately. This feature extraction technique allows for support-vector-machine classification and image analysis.RESULTS: Identification of AD patterns is improved when the latent symmetry of the brain is considered, with an estimated 92.78% accuracy (92.86% sensitivity, 92.68% specificity) using a linear kernel and a leave-one-out cross validation strategy. Also, asymmetries may be used to define a test for AD that is very specific (90.24% specificity) but not especially sensitive.CONCLUSIONS: Two main conclusions are derived from the analysis of the eigenimage spectrum. Firstly, the recognition of AD patterns is improved when considering only the symmetric part of the spectrum. Secondly, asymmetries in the hypo-metabolic patterns, when present, are more pronounced in subjects with AD.

Resumo Limpo

jectiv paper explor import latent symmetri brain computeraid system diagnos alzheim diseas ad symmetri asymmetri studi two point view develop effect classifi within scope machin learn techniqu ii assess relev ad diagnosi earli stage diseasemethod propos methodolog base eigenimag decomposit singlephoton emissioncomput tomographi imag use eigenspac extens accommod odd even eigenvector separ featur extract techniqu allow supportvectormachin classif imag analysisresult identif ad pattern improv latent symmetri brain consid estim accuraci sensit specif use linear kernel leaveoneout cross valid strategi also asymmetri may use defin test ad specif specif especi sensitiveconclus two main conclus deriv analysi eigenimag spectrum first recognit ad pattern improv consid symmetr part spectrum second asymmetri hypometabol pattern present pronounc subject ad

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