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Effectiveness of a vision-based handle trajectory monitoring system in studying robotic suture operation

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Abstract

Data on surgical robots are not openly accessible, limiting further study of the operation trajectory of surgeons’ hands. Therefore, a trajectory monitoring system should be developed to examine objective indicators reflecting the characteristic parameters of operations. 20 robotic experts and 20 first-year residents without robotic experience were included in this study. A dry-lab suture task was used to acquire relevant hand performance data. Novices completed training on the simulator and then performed the task, while the expert team completed the task after warm-up. Stitching errors were measured using a visual recognition method. Videos of operations were obtained using the camera array mounted on the robot, and the hand trajectory of the surgeons was reconstructed. The stitching accuracy, robotic control parameters, balance and dexterity parameters, and operation efficiency parameters were compared. Experts had smaller center distance (p < 0.001) and larger proximal distance between the hands (p < 0.001) compared with novices. The path and volume ratios between the left and right hands of novices were larger than those of experts (both p < 0.001) and the total volume of the operation range of experts was smaller (p < 0.001). The surgeon trajectory optical monitoring system is an effective and non-subjective method to distinguish skill differences. This demonstrates the potential of pan-platform use to evaluate task completion and help surgeons improve their robotic learning curve.

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Availability of data and materials

Data and materials are available on reasonable request from the corresponding author.

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Acknowledgements

The authors would like to thank all volunteers in this study.

Funding

This work was supported by the Provincial Teaching and Research Project of Colleges and Universities in Hubei Province (Grant No. 2020055) and the Zhongnan Hospital of Wuhan University Science, Technology and Innovation Seed Fund (Grant No. znpy2019003).

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Author notes

  1. Gaojie Chen and Lu Li contributed equally to this study and share the role of the first author.

  2. Kun Yang and ** Associated Laboratory, ZhongNan Hospital, Wuhan University, No. 169 Donghu Road, Wuhan, 430071, Hubei, China

    Gaojie Chen, Lu Li, Bin Luo, Kun Yang & **, and Remote Sensing, Wuhan University, Wuhan, Hubei, China

    Bin Luo

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  1. Gaojie Chen
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  2. Lu Li
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  3. Jacques Hubert
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  6. **nghuan Wang
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Contributions

Gaojie Chen and Lu Li wrote the main manuscript text; Jacques Hubert and Bin Luo provided guidance and technical support; Kun Yang and **nghuan Wang recruited volunteers and coordinate the project. All the authors reviewed the manuscript.

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Correspondence to Kun Yang or **nghuan Wang.

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Conflict of interest

Gaojie Chen, Lu Li, Jacques Hubert, Bin Luo, Kun Yang, and **nghuan Wang have no conflicts of interest to disclose.

Ethical approval

This study was approved by the Medical Ethics Committee of Zhongnan Hospital of Wuhan University (2023114K). All the participants provided informed consent and volunteered to participate in this study.

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Chen, G., Li, L., Hubert, J. et al. Effectiveness of a vision-based handle trajectory monitoring system in studying robotic suture operation. J Robotic Surg 17, 2791–2798 (2023). https://doi.org/10.1007/s11701-023-01713-9

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  • DOI: https://doi.org/10.1007/s11701-023-01713-9

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