Title

Detection of severity in Alzheimer’s disease (AD) using computational modeling

Authors

Hyunjo Kim

Affiliation

¹Department of Life Science, University of Gachon, Seungnam, Kyeonggido, Korea;

²Medical Informatics Department of Ajou Medical Center, South Korea

Email

hyunjokim@hotmail.com

Article Type

Hypothesis

Date

Received May 9, 2018; Revised May 9, 2018; Accepted May 19, 2018; Published May 31, 2018

The prevalent cause of dementia - Alzheimer's disease (AD) is characterized by an early cholinergic deficit that is in part responsible for the cognitive deficits (especially memory and attention defects). Prolonged AD leads to moderate-to-severe AD, which is one of the leading causes of death. Placebo-controlled, randomized clinical trials have shown significant effects of Acetyl cholin esterase inhibitors (ChEIs) on function, cognition, activities of daily living (ADL) and behavioral symptoms in patients. Studies have shown comparable effects for ChEIs in patients with moderate-to-severe or mild AD. Setting a fixed measurement (e.g. a Mini-Mental State Examination score, as a 'when to stop treatment limit) for the disease is not clinically rational. Detection of changed regional   cerebral   blood   flow   in   mild   cognitive   impairment   and early AD by perfusion-weighted magnetic resonance imaging has been a challenge. The utility of perfusion-weighted magnetic resonance imaging (PW-MRI) for detecting changes in regional cerebral blood flow (rCBF) in patients with mild cognitive impairment (MCI) and early AD was evaluated. We describe a computer aided prediction model to determine the severity of AD using known data in literature. We designed an automated system for the determination of AD severity. It is used to predict the clinical cases and conditions with disagreements. The algorithm described is useful in clinical practice to validate diagnosis. 

Keywords

Alzheimer’s disease; mini mental state examination; acetyl cholin esterase inhibitors; MRI; K-means: algorithm simulation

Citation

Kim et al. Bioinformation 14(5): 259-264 (2018)

Edited by

P Kangueane

Supplementary file

97320630014259S1.pdf

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.