Paper:
Development of a System for Determining Technique Level of Vascular Anastomosis Using Hand Motion
Xiaoshuai Chen*1 , Taro Shoji*1, Ryosuke Kowatari*2 , Koki Ebina*3 , Yoshifumi Kobayashi*1, Moeki Kato*1, Hinaha Kabasawa*4, Taisei Suzuki*4, Kazuya Sase*5 , Teppei Tsujita*6 , Shunsuke Komizunai*7 , Kazuhiko Oka*1, and Atsushi Konno*3
*1Graduate School of Science and Technology, Hirosaki University
3 Bunkyo-cho, Hirosaki, Aomori 036-8561, Japan
*2School of Medicine, Hirosaki University
5 Zaifu-cho, Hirosaki, Aomori 036-8562, Japan
*3Graduate School of Information Science and Technology, Hokkaido University
Kita 14 Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0814, Japan
*4Faculty of Science and Technology, Hirosaki University
3 Bunkyo-cho, Hirosaki, Aomori 036-8561, Japan
*5Faculty of Engineering, Tohoku Gakuin University
2-1-1 Tenjinzawa, Izumi-ku, Sendai, Miyagi 981-3193, Japan
*6Department of Mechanical Engineering, National Defense Academy of Japan
1-10-20 Hashirimizu, Yokosuka, Kanagawa 239-8686, Japan
*7Faculty of Engineering and Design, Kagawa University
2217-20 Hayashi-cho, Takamatsu, Kagawa 761-0396, Japan
In off-pump coronary artery bypass grafting (OPCAB), the coronary arteries are joined as the heart beats. This procedure requires high skill and experience to be performed reliably and quickly. Although training kits are commonly used for technical training, the inability of trainees to always be with experienced surgeons for guidance and to receive immediate evaluation remains problematic. To address this problem, a system that allows a single trainee to observe and quantitatively evaluate the procedures performed by an experienced surgeon is being developed. In this study, to analyze the differences between the motions of experienced and novice surgeons, Leap Motion was used to measure the hand motion of the vascular anastomosis performed by both surgeons using a training kit. Using the measured data, the features of the vascular anastomosis surgical techniques performed by experienced and novices were tested using the Mann–Whitney U test. In addition, a binary classification was performed using machine learning with these features. The binary classification results show that trainees can be classified as experts or novices with high accuracy using the developed system.
- [1] G. Kuwahara and T. Tashiro, “Current status of off-pump coronary artery bypass,” Annals of Thoracic and Cardiovascular Surgery, Vol.26, No.3, pp. 125-132, 2020. https://doi.org/10.5761/atcs.ra.18-00286
- [2] A. Neumann, L. Serna-Higuita, H. Detzel, A.-F. Popov, T. Krüger, L. Vöhringer, and C. Schlensak, “Off-pump coronary artery bypass grafting for patients with severely reduced ventricular function – A justified strategy?,” J. of Cardiac Surgery, Vol.37, No.1, pp. 7-17, 2022. https://doi.org/10.1111/jocs.15259
- [3] H. Nishi, H. Yokoyama, H. Yaku, K. Doi, Y. Nishimura, K. Abe, H. Tsukui, M. Tabata, K. Okamoto, Y.-K. Park et al., “Efficacy of simulation training for beating heart coronary anastomosis using beat+ youcan simulator,” Asian Cardiovascular and Thoracic Annals, Vol.30, No.6, pp. 661-668, 2022. https://doi.org/10.1177/02184923211060214
- [4] J. Ito, T. Shimamoto, G. Sakaguchi, and T. Komiya, “Impact of novel off-pump coronary artery bypass simulator on the surgical training,” General Thoracic and Cardiovascular Surgery, Vol.61, pp. 270-273, 2013. https://doi.org/10.1007/s11748-013-0211-y
- [5] J. Nitta, T. Akai, K. Miyahara, K. Hanada, and K. Hoshina, “Original homebuilt off-the-job training system for vascular surgeons: System analysis and assessment,” Annals of Vascular Diseases, Vol.11, No.4, pp. 525-530, 2018. https://doi.org/10.3400/avd.oa.18-00075
- [6] K. Miyahara, K. Hoshina, T. Akai, T. Isaji, K. Yamamoto, and T. Takayama, “Development of a web application that evaluates suture performance in off-the-job training,” Annals of Vascular Diseases, Vol.13, No.1, pp. 52-55, 2020. https://doi.org/10.3400/avd.oa.19-00108
- [7] S. Yasuda, J. van den Eynde, K. Vandendriessche, M. Masuda, B. Meyns, and W. Oosterlinck, “Implementation of a beating heart system for training in off-pump and minimally invasive coronary artery bypass,” BMC Surgery, Vol.21, No.1, pp. 1-8, 2021. https://doi.org/10.1186/s12893-020-01023-z
- [8] Z. Wlasitsch-Nagy, A. Bálint, A. Kőnig-Péter, P. Varga, E. Várady, P. Bogner, and B. Gasz, “New computational fluid dynamics-based method for morphological and functional assessment in cardiovascular skill training,” J. of Vascular Surgery Cases, Innovations and Techniques, Vol.8, No.4, pp. 770-778, 2022. https://doi.org/10.1016/j.jvscit.2022.09.012
- [9] K. Ahmed, D. Miskovic, A. Darzi, T. Athanasiou, and G. B. Hanna, “Observational tools for assessment of procedural skills: A systematic review,” The American J. of Surgery, Vol.202, No.4, pp. 469-480, 2011. https://doi.org/10.1016/j.amjsurg.2010.10.020
- [10] J. L. Larson, R. G. Williams, J. Ketchum, M. L. Boehler, and G. L. Dunnington, “Feasibility, reliability and validity of an operative performance rating system for evaluating surgery residents,” Surgery, Vol.138, No.4, pp. 640-649, 2005. https://doi.org/10.1016/j.surg.2005.07.017
- [11] S. Matsumoto, H. Kawahira, K. Oiwa, Y. Maeda, A. Nozawa, A. K. Lefor, Y. Hosoya, and N. Sata, “Laparoscopic surgical skill evaluation with motion capture and eyeglass gaze cameras: A pilot study,” Asian J. of Endoscopic Surgery, Vol.15, No.3, pp. 619-628, 2022. https://doi.org/10.1111/ases.13065
- [12] L. Yang, K. Lyu, and C. Song, “Application of an optical tracking system for motor skill assessment in laparoscopic surgery,” Computational and Mathematical Methods in Medicine 2022, 2022. https://doi.org/10.1155/2022/2332628
- [13] K. Ebina, T. Abe, K. Hotta, M. Higuchi, J. Furumido, N. Iwahara, M. Kon, K. Miyaji, S. Shibuya, Y. Lingbo et al., “Objective evaluation of laparoscopic surgical skills in wet lab training based on motion analysis and machine learning,” Langenbeck’s Archives of Surgery, Vol.407, No.5, pp. 2123-2132, 2022. https://doi.org/10.1007/s00423-022-02505-9
- [14] K. Ebina, T. Abe, K. Hotta, M. Higuchi, J. Furumido, N. Iwahara, M. Kon, K. Miyaji, S. Shibuya, Y. Lingbo et al., “Automatic assessment of laparoscopic surgical skill competence based on motion metrics,” Plos one, Vol.17, No.11, Article No.e0277105, 2022. https://doi.org/10.1371/journal.pone.0277105
- [15] S. Tsuyuki, K. Miyahara, K. Hoshina, T. Kawahara, M. Suhara, Y. Mochizuki, R. Taniguchi, and T. Takayama, “Motion capture device reveals a quick learning curve in vascular anastomosis training,” Surgery Today, Vol.54, No.3, pp. 275-281, 2023. https://doi.org/10.1007/s00595-023-02726-5
- [16] S. Tsuyuki, K. Hoshina, K. Miyahara, M. Suhara, M. Matsukura, T. Isaji, and T. Takayama, “Motion analysis of suturing technique with leap motion controller™. Proof-of-concept,” Science Progress, Vol.105, No.2, Article No.00368504221103777, 2022. https://doi.org/10.1177/00368504221103777
- [17] F. Weichert, D. Bachmann, B. Rudak, and D. Fisseler, “Analysis of the accuracy and robustness of the leap motion controller,” Sensors, Vol.13, No.5, pp. 6380-6393, 2013. https://doi.org/10.3390/s130506380
- [18] P. P. Valentini and E. Pezzuti, “Accuracy in fingertip tracking using leap motion controller for interactive virtual applications,” Int. J. on Interactive Design and Manufacturing (IJIDeM), Vol.11, No.3, pp. 641-650, 2017. https://doi.org/10.1007/s12008-016-0339-y
- [19] G. Nagymáté and R. M. Kiss, “Application of OptiTrack Motion Capture Systems in Human Movement Analysis: A systematic Literature Review,” Recent Innovations in Mechatronics, Vol.5, No.1, pp. 1-9, 2018. https://doi.org/10.17667/riim.2018.1/13
- [20] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. of Machine Learning Research, Vol.12, pp. 2825-2830, 2011.
- [21] B. E. Boser, I. M. Guyon, and V. N. Vapnik, “A training algorithm for optimal margin classifiers,” Proc. of the fifth Annual Workshop on Computational Learning Theory, pp. 144-152, 1992. https://doi.org/10.1145/130385.130401
- [22] S. Varma and R. Simon, “Bias in error estimation when using cross-validation for model selection,” BMC Bioinformatics, Vol.7, No.1, Article No.91, 2006. https://doi.org/10.1186/1471-2105-7-91
- [23] Y.-W. Chang, C.-J. Hsieh, K.-W. Chang, M. Ringgaard, and C.-J. Lin, “Training and testing low-degree polynomial data mappings via linear svm,” J. of Machine Learning Research, Vol.11, No.4, pp. 1471-1490, 2010.
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