[1]时承,姚旭峰.机器学习用于帕金森病诊断的研究进展[J].中国医学物理学杂志,2024,41(5):640-645.[doi:DOI:10.3969/j.issn.1005-202X.2024.05.016]
　SHI Cheng,YAO Xufeng.Advances in machine learning for the diagnosis of Pakinsons disease[J].Chinese Journal of Medical Physics,2024,41(5):640-645.[doi:DOI:10.3969/j.issn.1005-202X.2024.05.016]
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机器学习用于帕金森病诊断的研究进展()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第5期
页码:
640-645
栏目:
医学人工智能
出版日期:
2024-05-23

文章信息/Info

Title:
Advances in machine learning for the diagnosis of Pakinsons disease
文章编号:
1005-202X（2024）05-0640-06
作者:
时承1; 2; 姚旭峰2
1.上海理工大学健康科学与工程学院， 上海 200093； 2.上海健康医学院医学影像学院， 上海 201318
Author(s):
SHI Cheng1;  2;  YAO Xufeng2
1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 2. School of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
关键词:
帕金森病; 机器学习; 智能诊断; 综述
Keywords:
Parkinsons disease machine learning intelligent diagnosis review
分类号:
R318；R742.5
DOI:
DOI:10.3969/j.issn.1005-202X.2024.05.016
文献标志码:
A
摘要:
帕金森病（PD）是仅次于阿尔茨海默病的第二大神经退行性疾病，早期的诊断和干预对患者至关重要。本文聚焦于机器学习对于PD的智能诊断，介绍了在PD诊断中的常见机器学习算法，重点介绍了卷积神经网络和长短期记忆网络。此外，文章还详细介绍了其在医学图像分析和运动行为分析中的应用，通过比较国内外的相关研究，分析了使用不同的影像学和运动学数据进行PD诊断的优缺点，最后对机器学习用于PD诊断进行了总结与展望。
Abstract:
Parkinsons disease (PD) is the second most common neurodegenerative disease after Alzheimers disease, and the early diagnosis and intervention are crucial for patients. The review focuses on machine learning for intelligent diagnosis of PD. The common machine learning algorithms in PD diagnosis, specifically convolutional neural networks and long short-term memory networks, are introduced, and their applications in medical image analysis and motor behavior analysis are discussed in details. By comparing relevant domestic and international researches, the advantages and disadvantages of using different imaging and kinematic data for PD diagnosis are analyzed. Finally, the review summarizes and presents a prospect for the application of machine learning in PD diagnosis.

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备注/Memo

备注/Memo:
【收稿日期】2023-11-09 【基金项目】国家重点研发计划（2020YFC2008700）；国家自然科学基金（61971275，81830052）；上海市科学技术委员会地方院校能力建设项目（23010502700）。 【作者简介】时承，硕士研究生，主要研究方向：图像处理，E-mail: 223332549@st.usst.edu.cn 【通信作者】姚旭峰，教授，博士生导师，主要研究方向为医学影像处理、影像基因组学、人工智能，E-mail: yao6636329@hotmail.com
更新日期/Last Update: 2024-05-24