一个深度的多流模型,用于在2D心声回声学中对左心室射出分数进行可靠的预测
Jennifer Alvén1,2, Eva Hagberg3,4, David Hagerman5,3,4
1Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden. alven@chalmers.se.
Scientific reports
|January 24, 2024
概括
本研究引入了一种深度学习模型,用于从二维心声图中预测左心室喷射率 (LVEF). 该自动化系统实现了高精度,为心脏评估提供了强大的工具.
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:
- 准确预测左心室喷射率 (LVEF) 对于诊断和管理心脏疾病至关重要.
- 目前用于从二维心声回声 (2DE) 进行LVEF预测的自动化方法面临着数据变化和质量方面的挑战.
研究的目的:
- 开发和验证一个强大的深度多流模型,用于使用2DE检查进行自动化LVEF预测.
- 提高模型对临床心声回声图数据中常见问题的弹性,例如缺失或错误的视图.
主要方法:
- 采用了集结卷积神经网络和变压器层的深度多流模型.
- 该模型从四个自动选择的标准2DE视图中处理了八个数据流 (图像和光流).
- 通过预训练,战略抽样和参数共享来处理数据缺陷,实现了稳健性.
主要成果:
- 该模型在大型临床数据集 (12,648项检查) 上表现出强的表现,报告了[公式:参见文本]的R平方值和4.0%的平均绝对误差.
- 在公共基准上,拟议的模型与现有的完全自动化的LVEF预测方法相匹配或超越.
- 该系统在各种数据质量,检查医生和超声波系统变异方面都表现出强大.
结论:
- 开发的深度多流模型为2DE.的LVEF预测提供了可靠和准确的自动化解决方案.
- 该模型的稳定性和公开基准的表现表明其临床应用的潜力.
- 开源代码和训练有素的模型促进了自动回声心脏分析的进一步研究和开发.
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