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自动心肌对比回声心脏图像质量评估使用深度学习:对心肌 perfusion 评估的影响
Mingqi Li1, Dewen Zeng2, Hongwen Fei3
1Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, Wuhan, China.
Ultrasound in medicine & biology
|July 26, 2023
概括
这项研究开发了一种深度神经网络 (DNN),用于自动心肌对比回声心脏学 (MCE) 图像质量评估. DNN提高了诊断的一致性,并减少了医疗保健专业人员心肌输液评估的变化.
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:
- 心肌对比回声心脏图像 (MCE) 的图像质量对于准确的心肌 perfusion 评估至关重要.
- 图像质量不一致可能导致心声回声仪的诊断变化.
- 需要自动化MCE质量评估以提高诊断精度.
研究的目的:
- 开发和验证一个深度神经网络 (DNN) 用于自动MCE图像质量评估.
- 调查基于DNN的质量评分对心肌输液评估的影响.
- 评估质量评分变化对诊断一致性的影响.
主要方法:
- 一个Resnet-18模型进行了训练和测试,用于MCE质量评估,评估左心室变暗,阴影和闪光充足度.
- 从图像质量参数中得出质量评分.
- 分析了DNN质量得分和得分差异对各种经验丰富的心声回声学家对 perfusion 评估的影响.
主要成果:
- 在质量评估中,DNN在快速预测时间 (0.045秒/) 的情况下实现了高精度 (>90% F1分数).
- 较低的DNN质量评分与不可解释的细分的比例增加相关.
- 图像质量得分降低和扫描之间的得分差异较大,在输液评估中观察者间和观察者内部的一致性下降.
结论:
- 深度神经网络对于实时,自动的MCE质量评估是有效的.
- 这项技术有可能显著减少心肌输液评估中的诊断变异性.
- 自动化质量评估可以标准化MCE的解释,改善临床决策.
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