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      A Respiratory Motion Prediction Method Based on LSTM-AE with Attention Mechanism for Spine Surgery

      research-article
      1 , 2 , 3 , 3 , 1 , 2 , 4 , 1 , 2 , * , , 1 , 3
      Cyborg and Bionic Systems
      AAAS

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          Abstract

          Respiratory motion-induced vertebral movements can adversely impact intraoperative spine surgery, resulting in inaccurate positional information of the target region and unexpected damage during the operation. In this paper, we propose a novel deep learning architecture for respiratory motion prediction, which can adapt to different patients. The proposed method utilizes an LSTM-AE with attention mechanism network that can be trained using few-shot datasets during operation. To ensure real-time performance, a dimension reduction method based on the respiration-induced physical movement of spine vertebral bodies is introduced. The experiment collected data from prone-positioned patients under general anaesthesia to validate the prediction accuracy and time efficiency of the LSTM-AE-based motion prediction method. The experimental results demonstrate that the presented method (RMSE: 4.39%) outperforms other methods in terms of accuracy within a learning time of 2 min. The maximum predictive errors under the latency of 333 ms with respect to the x, y, and z axes of the optical camera system were 0.13, 0.07, and 0.10 mm, respectively, within a motion range of 2 mm.

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          Most cited references29

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          Accuracy of robot-assisted placement of lumbar and sacral pedicle screws: a prospective randomized comparison to conventional freehand screw implantation.

          Single-center prospective randomized controlled study.
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            Accuracy of Pedicle Screw Placement and Clinical Outcomes of Robot-assisted Technique Versus Conventional Freehand Technique in Spine Surgery From Nine Randomized Controlled Trials: A Meta-analysis.

            A meta-analysis.
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              Methods to determine pedicle screw placement accuracy in spine surgery: a systematic review.

              Systematic review.

                Author and article information

                Journal
                Cyborg Bionic Syst
                Cyborg Bionic Syst
                CBSYSTEMS
                Cyborg and Bionic Systems
                AAAS
                2097-1087
                2692-7632
                05 January 2024
                2024
                : 5
                : 0063
                Affiliations
                [ 1 ]School of Medical Technology, Beijing Institute of Technology , Beijing, China.
                [ 2 ]School of Mechatronical Engineering, Beijing Institute of Technology , Beijing, China.
                [ 3 ]Ji Shui Tan Hospital, Beijing, China.
                [ 4 ]Department of Radiation Oncology, Stanford University , Stanford, CA, USA.
                Author notes
                [*] [* ]Address correspondence to: duanstar@ 123456bit.edu.cn
                Article
                0063
                10.34133/cbsystems.0063
                10769044
                38188983
                ae113f09-7e3c-4a67-8eac-9a11ff973ce3
                Copyright © 2024 Zhe Han et al.

                Exclusive licensee Beijing Institute of Technology Press. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0).

                History
                : 08 May 2023
                : 21 September 2023
                : 05 January 2024
                Page count
                Figures: 9, Tables: 3, References: 33, Pages: 0
                Categories
                Research Article

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