器官搭载机器人的冠状动脉检测方法。
Coronary vessel detection methods for organ-mounted robots.
机构信息
Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Cardiovascular Institute, Allegheny Health Network, Pittsburgh, Pennsylvania, USA.
出版信息
Int J Med Robot. 2021 Oct;17(5):e2297. doi: 10.1002/rcs.2297. Epub 2021 Jun 14.
BACKGROUND
HeartLander is a tethered robot walker that utilizes suction to adhere to the beating heart. HeartLander can be used for minimally invasive administration of cardiac medications or ablation of tissue. In order to administer injections safely, HeartLander must avoid coronary vasculature.
METHODS
Doppler ultrasound signals were recorded using a custom-made cardiac phantom and used to classify different coronary vessel properties. The classification was performed by two machine learning algorithms, the support vector machines and a deep convolutional neural network. These algorithms were then validated in animal trials.
RESULTS
Accuracy of identifying vessels above turbulent flow reached greater than 92% in phantom trials and greater than 98% in animal trials.
CONCLUSIONS
Through the use of two machine learning algorithms, HeartLander has shown the ability to identify different sized vasculature proximally above turbulent flow. These results indicate that it is feasible to use Doppler ultrasound to identify and avoid coronary vasculature during cardiac interventions using HeartLander.
背景
HeartLander 是一种通过吸附作用附着在跳动心脏上的系绳机器人。它可用于微创给药或组织消融。为了安全地进行注射，HeartLander 必须避开冠状动脉血管。
方法
使用定制的心脏模型记录多普勒超声信号，并用于对不同冠状动脉血管特性进行分类。通过两种机器学习算法，支持向量机和深度卷积神经网络来进行分类。然后在动物试验中进行验证。
结果
在模型试验中，识别湍流上方血管的准确率大于 92%，在动物试验中，准确率大于 98%。
结论
通过使用两种机器学习算法，HeartLander 已经显示出能够识别湍流上方不同大小血管的能力。这些结果表明，在使用 HeartLander 进行心脏介入时，使用多普勒超声来识别和避开冠状动脉血管是可行的。
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