在使用卷积神经网络的心声心电图中检测心脏相位
Moomal Farhad1, Mohammad Mehedy Masud2, Azam Beg1
1College of Information Technology, United Arab Emirates University, Al Ain, P.O. Box 15551, United Arab Emirates.
Scientific reports
|June 1, 2023
概括
深度学习模型DeepPhase自动化了心声心电图中的心脏相位检测,提高了心脏病学家的准确性和效率. 这种人工智能工具可以识别末缩 (ES) 和末腹缩 (ED) 阶段,减少心脏状况评估中的手动错误.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 心声谱对于评估心脏病状况至关重要,它依赖于准确识别心脏末缩 (ES) 和末腹缩 (ED) 阶段.
- 手动检测这些阶段是耗时的,容易出现诊断错误,需要自动化解决方案.
- 非必要的框架 (非ESED) 可以使手动阶段识别复杂化.
研究的目的:
- 开发和评估DeepPhase,这是一种用于自动检测ES,ED和非ESED心脏阶段的深度学习模型.
- 提高心脏相位识别的效率和准确性,从而最大限度地减少诊断错误.
- 提供一种工具,帮助心脏病学人员分析心声图像.
主要方法:
- 一个卷积神经网络 (CNN) 模型,DeepPhase,是使用心声回声图像开发的.
- 该模型经过训练,可以识别ES,ED和非ESED阶段,而不需要左心室细分或心电图.
- 引入了一种新的裁剪技术,以提高模型性能和现实世界的适用性.
主要成果:
- 与现有技术相比,DeepPhase表现出优越的性能.
- 该模型在CAMUS数据集上实现了0.96的曲线下的面积 (AUC).
- 在新收集的心脏相位数据集上,AUC达到0.82.
结论:
- DeepPhase提供了一种自动化和高效的方法来检测心脏相位在心声回声学中.
- 该模型显示了减少诊断错误和改善心脏病学工作流程的巨大潜力.
- 建议的深度学习方法和裁剪技术在现实世界的临床应用中是有效的.
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