深度学习驱动的心声学创新:分类学,临床影响,挑战和机会
Patrice Monkam1,2, Xu Wang1, Shuang Liu3
1College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
Annals of biomedical engineering
|December 16, 2025
概括
人工智能 (AI),特别是深度学习 (DL),为心声回声 (回声) 图像分析中的挑战提供了解决方案. 本综述探讨了回声中的DL应用,强调了心血管疾病诊断的临床影响,挑战和未来方向.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- 尽管技术进步,心声回声 (回声) 系统在数据解释方面面临着挑战.
- 人工智能 (AI),特别是深度学习 (DL),正在探索解决这些回声解释挑战.
研究的目的:
- 分析利用DL转化心声回声的研究.
- 专注于在心声学中DL的临床影响,挑战和机会.
- 为DL驱动的心声回声创新提供基础.
主要方法:
- 在回声中对DL研究进行分类,分为数据采集/增强和智能数据分析.
- 关键任务的分析:注释数据生成,心脏异常诊断和心脏结构细分.
- 包括心脏解剖学,临床参数和DL模型实施细节.
主要成果:
- DL显著影响心血管疾病的自动诊断和监测.
- 广泛研究了诸如心脏结构细分和异常诊断等关键任务.
- 确定了DL驱动心声回声学中未被充分探索的挑战和潜在的解决方案.
结论:
- DL技术为心声学和心血管疾病管理提供了变革性的潜力.
- 跨学科的合作对于推动心声回声学中的DL至关重要.
- 这一综述为未来的DL驱动心声学研究和创新提供了基础.
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