Abstract
In recent years, medical images have been increasingly used as an objective method for the diagnosis of neurodegenerative diseases. Most previous studies have been based on structural or functional magnetic resonance imaging. However, the results are not yet sufficient to identify early stages of dementia. In this paper, we present an image processing and pattern recognition strategy that allows to predict short-term conversion to Mild Cognitive Impairment (MCI) based on the analysis of Arterial Spin Labeling images. Healthy subjects, categorized as individuals at risk of dementia, were assessed annually in order to identify those that converted to MCI. After 1 − 2 years, 20 subjects were classified as non-converters and 15 as converters according Mini–Mental State Examination test and other neuropsychiatric scales. The proposed approach was able to classify converter from non-converter subjects with an accuracy of 0.88 using the leave-one-out cross-validation method.
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Keywords
- Support Vector Machine
- Mild Cognitive Impairment
- Arterial Spin Label
- Medial Temporal Lobe Atrophy
- Mild Cognitive Impairment Subtype
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Díaz, G., García-Polo, P., Mato, V., Alfayate, E., Hernández-Tamames, J.A., Malpica, N. (2014). Predicting Very Early Stage Mild Cognitive Impairment Based on a Voxel-wise Arterial Spin Labeling Analysis. In: Bayro-Corrochano, E., Hancock, E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827. Springer, Cham. https://doi.org/10.1007/978-3-319-12568-8_87
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DOI: https://doi.org/10.1007/978-3-319-12568-8_87
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