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

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....

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Autor principal: Kim, Hyunjo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biomedical Informatics 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6077821/
https://www.ncbi.nlm.nih.gov/pubmed/30108425
http://dx.doi.org/10.6026/97320630014259
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author Kim, Hyunjo
author_facet Kim, Hyunjo
author_sort Kim, Hyunjo
collection PubMed
description 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 from specialist. The model described is useful in clinical practice to validate diagnosis.
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spelling pubmed-60778212018-08-14 Detection of severity in Alzheimer's disease (AD) using computational modeling Kim, Hyunjo Bioinformation Hypothesis 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 from specialist. The model described is useful in clinical practice to validate diagnosis. Biomedical Informatics 2018 -05-31 /pmc/articles/PMC6077821/ /pubmed/30108425 http://dx.doi.org/10.6026/97320630014259 Text en © 2018 Biomedical Informatics http://creativecommons.org/licenses/by/3.0/ 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.
spellingShingle Hypothesis
Kim, Hyunjo
Detection of severity in Alzheimer's disease (AD) using computational modeling
title Detection of severity in Alzheimer's disease (AD) using computational modeling
title_full Detection of severity in Alzheimer's disease (AD) using computational modeling
title_fullStr Detection of severity in Alzheimer's disease (AD) using computational modeling
title_full_unstemmed Detection of severity in Alzheimer's disease (AD) using computational modeling
title_short Detection of severity in Alzheimer's disease (AD) using computational modeling
title_sort detection of severity in alzheimer's disease (ad) using computational modeling
topic Hypothesis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6077821/
https://www.ncbi.nlm.nih.gov/pubmed/30108425
http://dx.doi.org/10.6026/97320630014259
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