关于检测和分类帕金森病的计算方法的审查
Komal Singh1, Manish Khare2, Ashish Khare1
1Department of Electronics and Communication, University of Allahabad, Prayagraj, India.
Computers in biology and medicine
|February 12, 2025
概括
准确的帕金森病 (PD) 诊断非常重要,因为发病率不断上升. 本综述探讨了先进的成像和机器学习技术,以改善PD的早期检测和治疗.
科学领域:
- 神经学 神经学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 帕金森病 (PD) 的发病率在25年内翻了一番,需要改进诊断方法.
- 目前的诊断方法缺乏足够的准确性,导致需要先进技术.
- 准确及时诊断对于有效的帕金森病管理至关重要.
研究的目的:
- 系统地审查使用成像模式诊断帕金森病的最新方法.
- 探索机器学习和深度学习在PD检测中的应用.
- 确定PD诊断工具的局限性和未来方向.
主要方法:
- 对用于检测帕金森病的成像方法的审查,包括解剖学和神经递质变化.
- 机器学习和深度学习模型的分析应用于PD诊断.
- 检查PD诊断现有数据集及其适用性.
主要成果:
- 大多数审查的算法都利用机器学习或深度学习来自动诊断帕金森病.
- 由于其可用性和简单性,通常使用T1加权的MRI数据.
- 深度学习模型显示了准确的PD诊断有希望的结果.
结论:
- 深度学习模型为提高帕金森病诊断的准确性提供了巨大的潜力.
- 完善现有方法是必要的,以克服挑战,提高诊断的准确性.
- 需要进一步的研究来开发更具歧视性的PD诊断特征集.
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