论文标题
机器人身体检查中的自动心脏和肺听诊
Automated Heart and Lung Auscultation in Robotic Physical Examinations
论文作者
论文摘要
本文介绍了心脏和肺部声音的自主机器人听诊的首次实施。为了选择产生高质量声音的听诊位置，贝叶斯优化（BO）公式利用视觉解剖学提示来预测可能位于高质量的声音的位置，同时使用听觉反馈来适应患者特定的解剖学质量。声音质量是使用在心脏和肺听诊器录音数据库中训练的机器学习模型在线估算的。对4个人类受试者进行的实验表明，与受过临床听诊的人类训练的人类手术相比，我们的系统自主捕获了心脏和肺部质量相似的心脏和肺部声音。令人惊讶的是，其中一名受试者表现出一种先前未知的心脏病理，该病理最初是使用我们的机器人鉴定出来的，该病理学证明了自主机器人助理学对健康筛查的潜在实用性。
This paper 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.
