A fully autonomous robotic ultrasound system for thyroid scanning
Kang Su,
Jingwei Liu,
Xiaoqi Ren,
Yingxiang Huo,
Guanglong Du (csgldu@scut.edu.cn),
Wei Zhao,
Xueqian Wang (wang.xq@sz.tsinghua.edu.cn),
Bin Liang (bliang@tsinghua.edu.cn),
Di Li and
Peter Xiaoping Liu (xpliu@sce.carleton.ca)
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Kang Su: South China University of Technology
Jingwei Liu: South China University of Technology
Xiaoqi Ren: South China University of Technology
Yingxiang Huo: South China University of Technology
Guanglong Du: South China University of Technology
Wei Zhao: Nanfang Hospital Southern Medical University
Xueqian Wang: Tsinghua University
Bin Liang: Tsinghua University
Di Li: South China University of Technology
Peter Xiaoping Liu: Carleton University
Nature Communications, 2024, vol. 15, issue 1, 1-18
Abstract:
Abstract The current thyroid ultrasound relies heavily on the experience and skills of the sonographer and the expertise of the radiologist, and the process is physically and cognitively exhausting. In this paper, we report a fully autonomous robotic ultrasound system, which is able to scan thyroid regions without human assistance and identify malignant nod- ules. In this system, human skeleton point recognition, reinforcement learning, and force feedback are used to deal with the difficulties in locating thyroid targets. The orientation of the ultrasound probe is adjusted dynamically via Bayesian optimization. Experimental results on human participants demonstrated that this system can perform high-quality ultrasound scans, close to manual scans obtained by clinicians. Additionally, it has the potential to detect thyroid nodules and provide data on nodule characteristics for American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) calculation.
Date: 2024
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DOI: 10.1038/s41467-024-48421-y
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