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Proceedings Paper

Finding models to detect Alzheimer's disease by fusing structural and neuropsychological information
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Paper Abstract

Alzheimer's disease (AD) is a neurodegenerative disease that affects higher brain functions. Initial diagnosis of AD is based on the patient's clinical history and a battery of neuropsychological tests. The accuracy of the diagnosis is highly dependent on the examiner's skills and on the evolution of a variable clinical frame. This work presents an automatic strategy that learns probabilistic brain models for different stages of the disease, reducing the complexity, parameter adjustment and computational costs. The proposed method starts by setting a probabilistic class description using the information stored in the neuropsychological test, followed by constructing the different structural class models using membership values from the learned probabilistic functions. These models are then used as a reference frame for the classification problem: a new case is assigned to a particular class simply by projecting to the different models. The validation was performed using a leave-one-out cross-validation, two classes were used: Normal Control (NC) subjects and patients diagnosed with mild AD. In this experiment it is possible to achieve a sensibility and specificity of 80% and 79% respectively.

Paper Details

Date Published: 22 December 2015
PDF: 9 pages
Proc. SPIE 9681, 11th International Symposium on Medical Information Processing and Analysis, 96810F (22 December 2015); doi: 10.1117/12.2211489
Show Author Affiliations
Diana L. Giraldo, Univ. Nacional de Colombia (Colombia)
Juan D. García-Arteaga, Univ. Nacional de Colombia (Colombia)
Nelson Velasco, Univ. Nacional de Colombia (Colombia)
Univ. Militar Nueva Granada (Colombia)
Eduardo Romero, Univ. Nacional de Colombia (Colombia)


Published in SPIE Proceedings Vol. 9681:
11th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Juan D. García-Arteaga; Jorge Brieva, Editor(s)

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