カミヤ　コウヘイ   Kamiya Kohei   神谷　昂平    所属   東邦大学  医学部 医学科（大森病院）    職種   講師
論文種別	原著
言語種別	英語
査読の有無	査読あり
表題	A Novel Deep Learning Approach with a 3D Convolutional Ladder Network for Differential Diagnosis of Idiopathic Normal Pressure Hydrocephalus and Alzheimer's Disease.
掲載誌名	正式名：Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
巻・号・頁	19(4),pp.351-358
著者・共著者	Ryusuke Irie,Yujiro Otsuka,Akifumi Hagiwara,Koji Kamagata,Kouhei Kamiya,Michimasa Suzuki,Akihiko Wada,Tomoko Maekawa,Shohei Fujita,Shimpei Kato,Madoka Nakajima,Masakazu Miyajima,Yumiko Motoi,Osamu Abe,Shigeki Aoki
発行年月	2020/12
概要	PURPOSE: Idiopathic normal pressure hydrocephalus (iNPH) and Alzheimer's disease (AD) are geriatric diseases and common causes of dementia. Recently, many studies on the segmentation, disease detection, or classification of MRI using deep learning have been conducted. The aim of this study was to differentiate iNPH and AD using a residual extraction approach in the deep learning method. METHODS: Twenty-three patients with iNPH, 23 patients with AD and 23 healthy controls were included in this study. All patients and volunteers underwent brain MRI with a 3T unit, and we used only whole-brain three-dimensional (3D) T1-weighted images. We designed a fully automated, end-to-end 3D deep learning classifier to differentiate iNPH, AD and control. We evaluated the performance of our model using a leave-one-out cross-validation test. We also evaluated the validity of the result by visualizing important areas in the process of differentiating AD and iNPH on the original input image using the Gradient-weighted Class Activation Mapping (Grad-CAM) technique. RESULTS: Twenty-one out of 23 iNPH cases, 19 out of 23 AD cases and 22 out of 23 controls were correctly diagnosed. The accuracy was 0.90.
DOI	10.2463/mrms.mp.2019-0106
PMID	31969525