基于人工智能对心声回声图视图的分类.
Jwan A Naser1, Eunjung Lee1, Sorin V Pislaru1
1Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
European heart journal. Digital health
|May 22, 2024
概括
使用卷积神经网络 (CNN) 的人工智能 (AI) 可以准确地分类心声回声图的视图. 这种自动化视图分类是将深度学习应用到心声回声学中的关键一步,改善了疾病检测.
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:
- 心声图分析可以通过人工智能进行增强,用于自动化参数评估和疾病模式识别.
- 准确的心脏视图分类是将深度学习算法应用于心声回声数据的先决条件.
研究的目的:
- 开发和评估卷积神经网络 (CNN) 用于对心声回应图的自动分类.
- 评估2D和3DCNN在胸前心声学 (TTE) 和护理点超声波 (POCUS) 数据集上的性能.
主要方法:
- 在909名患者的10269个TTE视频上训练了2D和3DCNN,以分类9个心脏视图类别.
- 在来自229名患者的2,582个TTE视频上内部验证了CNN.
- 在全面的TTE研究 (100名患者) 和POCUS视频 (408名患者) 上测试了CNN.
主要成果:
- 2D CNN在综合性TTE上获得了96.8%的准确度和0.997 AUC,在POCUS.US上获得了98.4%的准确度和0.998 AUC.
- 3D CNN在TTE上获得了96.3%的精度和0.998 AUC,在POCUS上获得了95.0%的精度和0.996 AUC.
- 使用2D CNNs的特定视图的积极预测值超过了93%.
结论:
- 使用CNN的自动心脏视图分类器证明了TTE和POCUS的高精度.
- 这种人工智能驱动的视图分类工具有助于将深度学习整合到心声回声学中.
- 开发的分类器可以提高心声回声分析的效率和准确性.
相关概念视频
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