Affiliations: [a] Biomedical Image Processing Group, Department of Electrical & Electronic Engineering, Eastern Mediterranean University, Gazimagusa, Mersin, Turkey
| [b]
British University of Nicosia, Ozankoy, Kyrinia, via Mersin, Turkey
Correspondence:
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Correspondence to: Iman Beheshti, Biomedical image processing group, Department of Electrical & Electronic Engineering. Eastern Mediterranean University, Gazimagusa, Mersin 10, Turkey. Tel.: +90 392 630 1301/ext 1093; Fax: +90 392 630 1648; E-mail: iman.beheshti@cc.emu.edu.tr.
Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
Abstract: Background: Recently, automatic risk assessment methods have been a target for the detection of Alzheimer’s disease (AD) risk. Objective: This study aims to develop an automatic computer-aided AD diagnosis technique for risk assessment of AD using information diffusion theory. Methods: Information diffusion is a fuzzy mathematics logic of set-value that is used for risk assessment of natural phenomena, which attaches fuzziness (uncertainty) and incompleteness. Data were obtained from voxel-based morphometry analysis of structural magnetic resonance imaging. Results and Conclusion: The information diffusion model results revealed that the risk of AD increases with a reduction of the normalized gray matter ratio (p > 0.5, normalized gray matter ratio <40%). The information diffusion model results were evaluated by calculation of the correlation of two traditional risk assessments of AD, the Mini-Mental State Examination and the Clinical Dementia Rating. The correlation results revealed that the information diffusion model findings were in line with Mini-Mental State Examination and Clinical Dementia Rating results. Application of information diffusion model contributes to the computerization of risk assessment of AD, which has a practical implication for the early detection of AD.
Keywords: Alzheimer’s disease, computer-aided AD diagnosis, early detection, gray matter volume, information diffusion theory, risk assessment
