基于机器学习的方法用于超声波视频片段中检测心脏病
Netzahualcoyotl Hernandez-Cruz1, Olga Patey2, Bojana Salovic2
1Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, UK.
Computer methods and programs in biomedicine
|August 23, 2025
概括
这项研究引入了一种人工智能方法, 用超声波视频自动检测心脏缺陷. 这个系统准确地识别胎儿的解剖结构, 并评估心脏位置, 以更早的诊断.
科学领域:
- 医学成像
- 人工智能
- 胎儿发育情况
背景情况:
- 这是一种先天性异常, 心脏位于胸部右侧.
- 准确的产前检测对于及时干预和管理至关重要.
- 目前的检测方法可能受到专业知识和可访问性的限制.
研究的目的:
- 开发和验证基于机器学习的自动检测心脏病的方法.
- 分析超声波 (美国) 横向视频,特别是Situs和四腔 (4CH) 视图.
- 提高产前心脏检查的一致性和可访问性.
主要方法:
- 使用基于变压器的细分模型 (SegFormer) 对胎儿的解剖结构 (胸部,脊椎,胃,心脏) 进行分析.
- 实施质量评估模块来验证信息视频.
- 通过分析胃和心脏与胎儿胸部的相对方向来确定心脏.
主要成果:
- 实现了对分段的高分数 (0.949-0.968) 和对中心点估计的低RMSE (0.23-0.39mm).
- 自动测定心动脉的结果是基于框架的分类得分 (FBCS) 为0. 99.
- 该方法在分类正常和右心病例方面表现强.
结论:
- 该自动化方法提供了准确的细分和可靠的美国视频内心检测.
- 它的简单采购协议和强大的管道使其适合非专家医疗保健提供者.
- 有潜力促进更早,更一致的产前心脏病诊断,特别是在资源有限的环境中.
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