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논문 기본 정보

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학술저널
저자정보
Kim JeeYoung (Department of Radiology Eunpyeong St. Mary’s Hospital College of Medicine The Catholic University o) Lee Minho (Research Institute NEUROPHET Inc.) Lee Min Kyoung (Department of Radiology Yeouido St. Mary’s Hospital College of Medicine The Catholic University of) Wang Sheng-Min (Department of Psychiatry Yeouido St. Mary’s Hospital College of Medicine The Catholic University of) Kim Nak-Young (Department of Psychiatry Yeouido St. Mary’s Hospital College of Medicine The Catholic University of) Kang Dong Woo (Department of Psychiatry Seoul St. Mary’s Hospital College of Medicine The Catholic University of K) Um Yoo Hyun (Department of Psychiatry St. Vincent’s Hospital Seoul College of Medicine The Catholic University o) Na Hae-Ran (Department of Psychiatry Yeouido St. Mary’s Hospital College of Medicine The Catholic University of) Woo Young Sup (Department of Psychiatry Yeouido St. Mary’s Hospital College of Medicine The Catholic University of) Lee Chang Uk (Department of Psychiatry Seoul St. Mary’s Hospital College of Medicine The Catholic University of K) Bahk Won-Myong (Department of Psychiatry Yeouido St. Mary’s Hospital College of Medicine The Catholic University of) Kim Donghyeon (Research Institute NEUROPHET Inc.) Lim Hyun Kook (Department of Psychiatry Yeouido St. Mary’s Hospital College of Medicine The Catholic University of)
저널정보
대한신경정신의학회 PSYCHIATRY INVESTIGATION PSYCHIATRY INVESTIGATION 제18권 제1호
발행연도
2021.1
수록면
69 - 79 (11page)

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초록· 키워드

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Objective Alzheimer’s disease (AD) is the most common type of dementia and the prevalence rapidly increased as the elderly population increased worldwide. In the contemporary model of AD, it is regarded as a disease continuum involving preclinical stage to severe dementia. For accurate diagnosis and disease monitoring, objective index reflecting structural change of brain is needed to correctly assess a patient’s severity of neurodegeneration independent from the patient’s clinical symptoms. The main aim of this paper is to develop a random forest (RF) algorithm-based prediction model of AD using structural magnetic resonance imaging (MRI).Methods We evaluated diagnostic accuracy and performance of our RF based prediction model using newly developed brain segmentation method compared with the Freesurfer’s which is a commonly used segmentation software.Results Our RF model showed high diagnostic accuracy for differentiating healthy controls from AD and mild cognitive impairment (MCI) using structural MRI, patient characteristics, and cognitive function (HC vs. AD 93.5%, AUC 0.99; HC vs. MCI 80.8%, AUC 0.88). Moreover, segmentation processing time of our algorithm (<5 minutes) was much shorter than of Freesurfer’s (6?8 hours).Conclusion Our RF model might be an effective automatic brain segmentation tool which can be easily applied in real clinical practice.
#Random forest #Alzheimer's disease #Mild cognitive impairment #Segmentation #MRI.

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참고문헌 (42)

참고문헌 신청
Oboudiyat C / 2013 / Alzheimer’s disease / Semin Neurol 33:313~329 google schola Lane CA / 2018 / Alzheimer’s disease / Eur J Neurol 25:59~70 google schola DeTure MA / 2019 / The neuropathological diagnosis of Alzheimer’s disease / Mol Neurodegener 14:32 google schola Robillard A / 2007 / Clinical diagnosis of dementia / Alzheimers Dement 3:292~298 google schola Sperling RA / 2011 / Toward defining the preclinical stages of Alzheimer’s disease : recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease / Alzheimers Dement 7:280~292 google schola

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