深度学习在心声学中用于增强检测左心室功能和壁运动异常
Manal Alhussein1, Michelle Xiang Liu2
1Department of Health Administration and Policy, Health Services Research / Discovery, Knowledge, and Health Informatics, College of Public Health, George Mason University, Fairfax, Virginia, United States.
Ultrasound in medicine & biology
|May 2, 2025
概括
深度学习 (DL) 通过提高测量准确性和异常识别来增强心血管疾病检测的回声心脏学. 临床采用需要解决数据质量和计算挑战,以提高诊断能力.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因之一.
- 早期检测和改进的诊断方法对于更好的心血管疾病治疗结果至关重要.
- 心声成像是评估心脏功能的关键成像方式.
研究的目的:
- 系统地审查深度学习 (DL) 在心声回声学中的整合,以检测心血管异常.
- 评估深层卷积神经网络 (DCNN) 在提高心声回声诊断精度方面的作用.
主要方法:
- 按照PRISMA 2020指南进行系统审查.
- 在IEEE Xplore,PubMed和Web of Science中进行全面的文献搜索.
- 对29项研究的分析,重点关注DL技术,特别是DCNN,在心声回声学中.
主要成果:
- 深度学习显著提高了检测和分类心声回声数据的准确性和可重复性.
- DL在提高左心室功能的测量和识别墙壁运动异常方面表现出色.
- 确定的挑战包括数据多样性,图像质量和DL模型的计算需求.
结论:
- 深度学习具有很大的潜力,可以提高心声回声仪的诊断能力.
- 成功的临床实施需要克服与数据质量,计算资源和系统集成有关的障碍.
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