Identification of milling status based on vibration signals using artificial intelligence in robot-assisted cervical laminectomy.

Journal: European journal of medical research
PMID: 37381061

Abstract

BACKGROUND: With advances in science and technology, the application of artificial intelligence in medicine has significantly progressed. The purpose of this study is to explore whether the k-nearest neighbors (KNN) machine learning method can identify three milling states based on vibration signals: cancellous bone (CCB), ventral cortical bone (VCB), and penetration (PT) in robot-assisted cervical laminectomy.

Authors

  • Rui Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • He Bai
    Department of Anorectal, Xinhua Hospital, Affiliated Hospital of Dalian University, Dalian 116021, China.
  • Guangming Xia
    Tianjin Key Laboratory of Intelligent Robotics, Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, 94 Weijin Road, Nankai District, Tianjin, 300071, China.
  • Jiaming Zhou
    Key Laboratory of Spine and Spinal Cord, Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, 300052, China.
  • Yu Dai
    College of Software, Northeastern University, Shenyang, China.
  • Yuan Xue
    Department of Nutrition and food hygiene, College of Public Health of Zhengzhou University, Zhengzhou, China, 450001. Electronic address: 962634470@qq.com.

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