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A fully autonomous robotic ultrasound system for thyroid scanning

Author

Listed:
  • Kang Su

    (South China University of Technology)

  • Jingwei Liu

    (South China University of Technology)

  • Xiaoqi Ren

    (South China University of Technology
    Peng Cheng Laboratory)

  • Yingxiang Huo

    (South China University of Technology
    Peng Cheng Laboratory)

  • 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)

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.

Suggested Citation

  • Kang Su & Jingwei Liu & Xiaoqi Ren & Yingxiang Huo & Guanglong Du & Wei Zhao & Xueqian Wang & Bin Liang & Di Li & Peter Xiaoping Liu, 2024. "A fully autonomous robotic ultrasound system for thyroid scanning," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48421-y
    DOI: 10.1038/s41467-024-48421-y
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    References listed on IDEAS

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    1. Daniel P. Zachs & Sarah J. Offutt & Rachel S. Graham & Yohan Kim & Jerel Mueller & Jennifer L. Auger & Nathaniel J. Schuldt & Claire R. W. Kaiser & Abigail P. Heiller & Raini Dutta & Hongsun Guo & Jam, 2019. "Noninvasive ultrasound stimulation of the spleen to treat inflammatory arthritis," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    2. Wenying Zhou & Yang Yang & Cheng Yu & Juxian Liu & Xingxing Duan & Zongjie Weng & Dan Chen & Qianhong Liang & Qin Fang & Jiaojiao Zhou & Hao Ju & Zhenhua Luo & Weihao Guo & Xiaoyan Ma & Xiaoyan Xie & , 2021. "Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    3. Qiang Zhang & Sheng Zhang & Yi Pan & Lin Sun & Jianxin Li & Yu Qiao & Jing Zhao & Xiaoqing Wang & Yixing Feng & Yanhui Zhao & Zhiming Zheng & Xiangming Yang & Lixia Liu & Chunxin Qin & Ke Zhao & Xiaon, 2022. "Deep learning to diagnose Hashimoto’s thyroiditis from sonographic images," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
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