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Intelligent system for alzheimer´s disease using neuroimaging


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Intelligent system for alzheimer´s disease using neuroimaging

Intelligent system for alzheimer´s disease using neuroimaging

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  • 1. Intelligent System for Early Detection of Alzheimer's disease using neuroimaging Domingo López Rodríguez Ricardo de Abajo llamero Antonio García Linares
  • 2. The diagnosis of Alzheimer's disease (AD) due to its evolution, occurs when neurological damage is present and is irreversible. The goal is to develop and implement an automated system for early detection of AD, by processing neuroimaging, and construction of automated and objective tools based in Artificial Intelligence and Data Mining.
  • 3. MEN WOMEN TOTAL HEALTHY 694 493 1187 MCI 348 434 782 AD 55 76 131 TOTAL 1097 1003 2100 Age range: from 18 to 96. MCI and AD were present in some subjects older than 55. Images were procedent from available MRI databases after passing a check to ensure the necessary quality
  • 4. Morphometric processing of these images was carried out using standard methodologies and packages such as SPM or FSL, besides our own developments. The results of this processing fed Computational Intelligence systems such as decision trees, support vector machines and genetic algorithms, apart from artificial neural networks, to develop a system to classify the state of the AD by neuroimaging.
  • 5. Parameter Value Correct Classification 91,48% Sensitivity 90,80% Specificity 92,30% Positive Predictive Value 0,886 Negative Predictive Value 0,939 To avoid over-training of the model, 10-fold cross validation was used. The resulting model incorporated SVMs, GGAA and Decision Trees.
  • 6. We have developed a computer system that is able to classify, based on structural neuroimaging studies, and with great accuracy, if the subject is in a normal state or have any chance of developing AD. It's a tool with great potential for application in early diagnosis of AD.
  • 7. Berr C, Vercambre MN, Akbaraly TN. Epidémiologie de la maladie d’Alzheimer: aspects méthodologiques et nouvelles perspectives. Psychol NeuroPsychiatr Vieil 2009 ; 7 (spécial) : 714. Flicker C, Ferris SH, Reisberg B. Mild cognitive impairment in the elderly: predictors of dementia. Neurology 1991; 41: 1006-9. Webb, G.I. (2007). Discovering Significant Patterns. Machine Learning 68(1). Netherlands: Springer, pages 1-33. Biomarkers for Alzheimer's disease. The research advances incrementally, but clinical use is still years away. Harv Ment Health Lett. 2010 Nov;27(5):1-3. Valls-Pedret C, Molinuevo JL, Rami. Diagnóstico precoz de la enfermedad de Alzheimer:fase prodrómica y preclínica. Rev Neurol 2010;51:471_80 K. Herrup. Reimagining Alzheimer’s Disease - An Age-Based Hypothesis. Journal of Neuroscience, 2010; 30 (50): 16755 Shaw LM, Vanderstichele H, Knapik-Czajka M, et al. Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Ann Neurol 2009; 65: 403–13.