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Deep learning-based video-analysis of instrument motion in microvascular anastomosis training

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Abstract

Purpose

Attaining sufficient microsurgical skills is paramount for neurosurgical trainees. Kinematic analysis of surgical instruments using video offers the potential for an objective assessment of microsurgical proficiency, thereby enhancing surgical training and patient safety. The purposes of this study were to develop a deep-learning-based automated instrument tip-detection algorithm, and to validate its performance in microvascular anastomosis training.

Methods

An automated instrument tip-tracking algorithm was developed and trained using YOLOv2, based on clinical microsurgical videos and microvascular anastomosis practice videos. With this model, we measured motion economy (procedural time and path distance) and motion smoothness (normalized jerk index) during the task of suturing artificial blood vessels for end-to-side anastomosis. These parameters were validated using traditional criteria-based rating scales and were compared across surgeons with varying microsurgical experience (novice, intermediate, and expert). The suturing task was deconstructed into four distinct phases, and parameters within each phase were compared between novice and expert surgeons.

Results

The high accuracy of the developed model was indicated by a mean Dice similarity coefficient of 0.87. Deep learning-based parameters (procedural time, path distance, and normalized jerk index) exhibited correlations with traditional criteria-based rating scales and surgeons’ years of experience. Experts completed the suturing task faster than novices. The total path distance for the right (dominant) side instrument movement was shorter for experts compared to novices. However, for the left (non-dominant) side, differences between the two groups were observed only in specific phases. The normalized jerk index for both the right and left sides was significantly lower in the expert than in the novice groups, and receiver operating characteristic analysis showed strong discriminative ability.

Conclusion

The deep learning-based kinematic analytic approach for surgical instruments proves beneficial in assessing performance in microvascular anastomosis. Moreover, this methodology can be adapted for use in clinical settings.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

We would like to thank Editage (www.editage.com) for English language editing.

We thank Masaki Mizuhara (Integra Japan Co., Ltd.), Kazuhiro Goto (Zeiss Japan Co., Ltd.), and Shusaku Tsutsui (Johnson & Johnson Inc.) for their support in organizing microvascular hands-on training courses.

Funding

This work was supported by the Japanese Society for the Promotion of Science KAKENHI Grant Number 21K09091.

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Author notes

  1. Taku Sugiyama and Hiroyuki Sugimori contributed equally to this work.

Authors and Affiliations

  1. Department of Neurosurgery, Hokkaido University Graduate School of Medicine, North 15 West 7, Kita-Ku, Sapporo, 060-8638, Japan

    Taku Sugiyama, Yasuhiro Ito, Masayuki Gekka, Haruto Uchino, Masaki Ito & Miki Fujimura

  2. Faculty of Health Sciences, Hokkaido University, Sapporo, 060-0812, Japan

    Hiroyuki Sugimori & Katsuhiko Ogasawara

  3. Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, North 15 West 7, Kita-Ku, Sapporo, 060-8638, Japan

    Minghui Tang

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  1. Taku Sugiyama
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Contributions

TS, HS, and MT contributed to the study conception and design. Data collection was performed by TS, YI, MG, HU, and MI. Data analysis was performed by HS and MT. KO and MF contributed to supervision of the study. The first draft of the manuscript was written by HS and TS. TS critically revised the manuscript, and all authors approved the final version of the manuscript.

Corresponding author

Correspondence to Taku Sugiyama.

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Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study was approved by the institutional review board of Hokkaido University Hospital, Sapporo, Japan (No. 018–0291).

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Informed consent was obtained from all individual participants included in this study.

Conflict of interest

The authors declare no competing interests.

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Sugiyama, T., Sugimori, H., Tang, M. et al. Deep learning-based video-analysis of instrument motion in microvascular anastomosis training. Acta Neurochir 166, 6 (2024). https://doi.org/10.1007/s00701-024-05896-4

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