Journal of Shandong University (Health Sciences) ›› 2023, Vol. 61 ›› Issue (3): 7-13.doi: 10.6040/j.issn.1671-7554.0.2022.1331

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Progresses and trends of intelligent technologies in orthopedic shock wave therapy

Yajun LIU1,2,Zhao LANG1,2,Anyi GUO1,2,Wenyong LIU3,4,*()   

  1. 1. National Center for Orthopedics, Beijing Jishuitan Hospital, Beijing 100035, China
    2. Beijing Research Institute of Traumatology and Orthopedics, Beijing 100035, China
    3. School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China
    4. Beijing Advanced Innovation Center for Biomedical Engineering, Beijing 100083, China
  • Received:2022-11-21 Online:2023-03-10 Published:2023-03-24
  • Contact: Wenyong LIU E-mail:wyliu@buaa.edu.cn

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Abstract:

Extracorporeal shock wave therapy (ESWT) is widely adopted in clinical orthopedics for its safe and non-invasive treatment. However, the experience-based subjective decision-making and long-lasting manual operation of physicians in the conventional orthopedic ESWT have limited its further development. As intelligent technologies are rapidly getting into the orthopedic ESWT, this review summarizes the state-of-the-art of research and application of intelligent technologies in orthopedic ESWT from aspects of computer navigation, machine learning and robotics. Computer navigation technologies can intuitively assist physicians to accurately locate the shock wave probe on the anatomical target of patients. The machine learning methods can automatically predict energy parameters in ESWT. Robotic systems have demonstrated their potential advantages in clinical efficacy especially in the dramatical alleviation of the operation intensity of physicians. These intelligent technologies provide comprehensive support for intellectualization of orthopedic ESWT from eye, brain and hand, respectively. This review also concludes the future technical trends from aspects of the ESWT biological mechanism and dose-effect relationship, the treatment protocol planning and usability of machine learning, and the treatment automation and robotic assistance.

Key words: Extracorporeal shock wave therapy, Orthopedics, Computer navigation, Machine learning, Medical robot

CLC Number: 

  • R454
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