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An automatic skill evaluation framework for robotic surgery training
Author(s) -
Peng Wenjia,
Xing Yuan,
Liu Ruida,
Li Jinhua,
Zhang Zemin
Publication year - 2019
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.1964
Subject(s) - dynamic time warping , computer science , robotic surgery , artificial intelligence , focus (optics) , hidden markov model , weakness , machine learning , medicine , surgery , physics , optics
Background To provide feedback to surgeons in robotic surgery training, many surgical skill evaluation methods have been developed. However, they hardly focus on the performance of the surgical motion segments. This paper proposes a method of specifying a trainee's skill weakness in the surgical training. Methods This paper proposed an automatic skill evaluation framework by comparing the trainees' operations with the template operation in each surgical motion segment, which is mainly based on dynamic time warping (DTW) and continuous hidden Markov model (CHMM). Results The feasibility of this proposed framework has been preliminarily verified. For specifying the skill weakness in instrument handling and efficiency, the result of this proposed framework was significantly correlated with that of manual scoring. Conclusion The automatic skill evaluation framework has shown its superiority in efficiency, objectivity, and being targeted, which can be used in robotic surgery training.