Automated Heart and Lung Auscultation in Robotic Physical Examinations
Automated Heart and Lung Auscultation in Robotic Physical Examinations
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DOI：
10.1109/lra.2022.3149576
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发表时间：
2022-04-01
影响因子：
5.2
通讯作者：
Hauser, Kris
中科院分区：
文献类型：
--
作者：
Zhu, Yifan;Smith, Alexander;Hauser, Kris
这封信介绍了第一个实现自主机器人听诊心脏和肺的声音。为了选择产生高质量声音的听诊位置，贝叶斯优化（BO）公式利用视觉解剖线索来预测高质量声音可能位于何处，同时使用听觉反馈来适应患者特定的解剖质量。使用在心肺听诊器记录数据库上训练的机器学习模型在线估计声音质量。在4名受试者身上进行的实验表明，与接受过临床听诊训练的人进行远程操作相比，我们的系统可以自主捕捉到质量相似的心肺声音。令人惊讶的是，其中一名受试者表现出一种以前未知的心脏病理，这是首次使用我们的机器人发现的，这表明了自主机器人听诊在健康筛查中的潜在效用。
This letter presents the first implementation of autonomous robotic auscultation of heart and lung sounds. To select auscultation locations that generate high-quality sounds, a Bayesian Optimization (BO) formulation leverages visual anatomical cues to predict where high-quality sounds might be located, while using auditory feedback to adapt to patient-specific anatomical qualities. Sound quality is estimated online using machine learning models trained on a database of heart and lung stethoscope recordings. Experiments on 4 human subjects show that our system autonomously captures heart and lung sounds of similar quality compared to tele-operation by a human trained in clinical auscultation. Surprisingly, one of the subjects exhibited a previously unknown cardiac pathology that was first identified using our robot, which demonstrates the potential utility of autonomous robotic auscultation for health screening.