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22 December 2015 Finding models to detect Alzheimer's disease by fusing structural and neuropsychological information
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Proceedings Volume 9681, 11th International Symposium on Medical Information Processing and Analysis; 96810F (2015) https://doi.org/10.1117/12.2211489
Event: 11th International Symposium on Medical Information Processing and Analysis (SIPAIM 2015), 2015, Cuenca, Ecuador
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.
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Diana L. Giraldo, Juan D. García-Arteaga, Nelson Velasco, and Eduardo Romero "Finding models to detect Alzheimer's disease by fusing structural and neuropsychological information", Proc. SPIE 9681, 11th International Symposium on Medical Information Processing and Analysis, 96810F (22 December 2015); https://doi.org/10.1117/12.2211489
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