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利用全面的回声数据为人工智能模型提供动力,用于手持心脏超声波
D M Anisuzzaman1, Jeffrey G Malins1, John I Jackson1
1Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.
Mayo Clinic proceedings. Digital health
|April 10, 2025
概括
一个新的深度学习框架准确地估计左心室喷射率 (LVEF),患者年龄和性别从心声视频. 这项技术显示出手持心脏超声波 (HCU) 诊断的前景.
科学领域:
- 人工智能在医学中的应用
- 心血管成像 - 心血管成像
- 深度学习应用程序
背景情况:
- 声心图对于评估心脏功能至关重要.
- 手持心脏超声波 (HCU) 提供了便携性,但需要强大的分析工具.
- 深度学习为增强心声回声分析提供了机会.
研究的目的:
- 开发一个端到端的深度学习框架,用于分析心声回声录像.
- 估计左心室喷射率 (LVEF),患者年龄,并对患者的性别进行分类.
- 评估框架在传统的胸前心电回声学 (TTE) 和手持心脏超声波 (HCU) 数据上的性能.
主要方法:
- 训练深度学习模型在一个大型回顾性TTE数据集 (6432项研究) 上.
- 对内部 (1369项研究) 和外部追溯TTE数据集 (4359项在三个地点的研究) 的验证模型.
- 将模型应用于包括HCU和TTE视频 (625名患者) 的前数据集.
主要成果:
- 在回顾性TTE数据上表现强,用于LVEF估计 (RMSE6.53-6.95%) 和分类 (AUC0.962-0.980).
- 精确的年龄估计 (RMSE 9.44%) 和性别分类 (AUC 0.882) 来自TTE.
- 在对LVEF,年龄和性别进行前性分析时,HCU和TTE的性能可比.
结论:
- 追溯的TTE数据有效地训练HCU应用程序的深度学习模型.
- 通过手持设备获得聚焦的诊断图像的可行性.
- 突出了深度学习在标准化和改进心脏超声波分析方面的潜力.
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