ArticleEuropean journal of medical research2023
Identification of milling status based on vibration signals using artificial intelligence in robot-assisted cervical laminectomy.
Article in European journal of medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- AI-driven tissue discrimination sensing methods for robot-assisted spine surgery: a state-of-the-art review.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Review
- Recent advances in robotic-assisted laminectomy in spine surgery: a narrative review.Asian spine journal · 2026Article
- Multisensory Integration for Identifying the Milling States in Robot-Assisted Cervical Laminectomy.Orthopaedic surgery · 2025Article
- Dura Vibration Difference Between PTP Bone Resection and Upper Facet Joints En Bolc Resection in Posterior Thoracic Decompression.Orthopaedic surgery · 2025Article
- Exploring the Impact of Muscle Vibration Therapy in Neurologic Rehabilitation: A Systematic Review.Archives of rehabilitation research and clinical translation · 2025Review
- Clustering Methods for Vibro-Acoustic Sensing Features as a Potential Approach to Tissue Characterisation in Robot-Assisted Interventions.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundWith 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.
methodsCervical laminectomies were performed on the cervical segments of eight pigs using a robot. First, the bilateral dorsal cortical bone and part of the CCB were milled with a 5 mm blade and then the bilateral laminae were milled to penetration with a 2 mm blade. During the milling process using the 2 mm blade, the vibration signals were collected by the acceleration sensor, and the harmonic components were extracted using fast Fourier transform. The feature vectors were constructed with vibration signal amplitudes of 0.5, 1.0, and 1.5 kHz and the KNN was then trained by the features vector to predict the milling states.
resultsThe amplitudes of the vibration signals between VCB and PT were statistically different at 0.5, 1.0, and 1.5 kHz (P < 0.05), and the amplitudes of the vibration signals between CCB and VCB were significantly different at 0.5 and 1.5 kHz (P < 0.05). The KNN recognition success rates for the CCB, VCB, and PT were 92%, 98%, and 100%, respectively. A total of 6% and 2% of the CCB cases were identified as VCB and PT, respectively; 2% of VCB cases were identified as PT.
conclusionsThe KNN can distinguish different milling states of a high-speed bur in robot-assisted cervical laminectomy based on vibration signals. This method is feasible for improving the safety of posterior cervical decompression surgery.
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