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科技导报
| 专题:阿尔茨海默病 2021, 39(20): 101-109
基于智能影像基因组学技术的阿尔茨海默病预测进展
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姚旭峰1 , 袁增贝1,2 , 卜溪溪1,2
作者信息
1. 上海健康医学院医学影像学院, 上海 201318;
2. 上海理工大学医疗器械与食品学院, 上海 200093
The early predication of Alzheimer's disease based on intelligent radiomics technology
Affiliations
出版时间: 2021-10-28
doi: 10.3981/j.issn.1000-7857.2021.20.009
文章导航
阿尔茨海默病(AD)起病隐匿,目前尚无有效控制病情进展以及治疗的药物或方法。在其发病轻度认知功能障碍阶段早期预测进行干预,则能有效控制其病程。从基于脑影像组学特征的AD早期临床诊断、基于人工智能影像组学技术的AD早期预测两个方面综述了AD的早期诊断与预测研究进展,提出结合多模脑核磁共振影像特征和组学特征,在深度学习的框架下,将影像学和基因组学联系在一起,构造高分类与预测性能的深度学习模型,可为AD早期筛查并干预提供支持。
The Alzheimer's disease (AD) is usually insidious in its onset and there are no drugs or methods to effectively control and treat the disease. Early prediction and intervention during the stage of the mild cognitive impairment (MCI) can effectively delay the course of the disease. The review contains two aspects. One is the early clinical diagnosis of the AD based on the brain imaging radiomics features, another is the AD early prediction based on the artificial intelligence (AI) of the imaging radiomics. We propose that in the framework of the deep learning, the imaging and the genomics are combined to construct a deep learning model with a high classification and prediction performance, to provide support for early screening and intervention of the AD.
radiomics
/
artificial intelligence
/
Alzheimer's disease
姚旭峰, 袁增贝, 卜溪溪.
基于智能影像基因组学技术的阿尔茨海默病预测进展.
科技导报,
2021
, 39
(20)
: 101
-109
.
DOI: 10.3981/j.issn.1000-7857.2021.20.009
YAO Xufeng, YUAN Zengbei, BU Xixi.
The early predication of Alzheimer's disease based on intelligent radiomics technology[J].
Science & Technology Review ,
2021
, 39
(20)
: 101
-109
.
DOI: 10.3981/j.issn.1000-7857.2021.20.009
2021年第39卷第20期
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文章信息
doi: 10.3981/j.issn.1000-7857.2021.20.009
接收时间:2020-04-08
首发时间:2021-11-08
出版时间:2021-10-28
收稿日期:2020-04-08
修回日期:2021-06-15
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2021.20.009
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2种不同金属材料的力学参数
科 Family 属数 Number of genus 种数 Number of species 占总种数比例 Percentage of total species (%) 属 Genus 种数 Number of species 占总种数比例 Percentage of total species (%) 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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