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心脏声音分析中的深度学习:从技术到临床应用
Qinghao Zhao1, Shijia Geng2, Boya Wang3
1Department of Cardiology, Peking University People's Hospital, Beijing, China.
Health data science
|October 10, 2024
概括
深度学习模型正在推进心脏声音分析,改善心脏异常检测. 本综述总结了数据集,技术和临床应用,强调了未来的研究需求,以实现更广泛的采用.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 心脏声音听觉对于诊断心脏疾病至关重要,但需要专家的解释.
- 深度学习 (DL) 在分析复杂的心声模式方面表现有前途,随着数据的可用性日益增加.
- 现有的评论缺乏心声分析中DL的全面摘要,重点是数据和临床应用.
研究的目的:
- 为DL模型培训审查可用的心声数据集.
- 在心脏声音分析中引入DL基础和最先进的技术.
- 总结DL在心声分析中的当前临床应用,包括限制和未来方向.
主要方法:
- 对心脏声音分析的深度学习的文献进行系统审查.
- 编制和分类公开可用的心脏声音数据集.
- 分析DL算法及其在临床应用中的性能.
主要成果:
- 详细概述常用的心声数据集及其特征.
- 应用于心声分析的DL技术的总结,包括卷积神经网络和循环神经网络.
- 探索DL在检测各种心脏病的应用,如声和心律失常.
结论:
- 深度学习集成显著推进心脏声音分析和临床实践.
- 越来越多的数据和改进的DL技术促进了这些模型的更广泛的临床采用.
- 进一步的研究对于克服挑战和提高DL在心脏病学中的临床实用性至关重要.
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