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Surgical Skill Evaluation From Robot-Assisted Surgery Recordings
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- Author(s) / Creator(s)
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Quality and safety are critical elements in the
performance of surgeries. Therefore, surgical trainees need to
obtain the required degrees of expertise before operating on
patients. Conventionally, a trainee’s performance is evaluated
by qualitative methods that are time-consuming and prone to
bias. Using autonomous and quantitative surgical skill assessment
improves the consistency, repeatability, and reliability
of the evaluation. To this end, this paper proposes a videobased
deep learning framework for surgical skill assessment.
By incorporating prior knowledge on surgeon’s activity in
the system design, we decompose the complex task of spatiotemporal
representation learning from video recordings into
two independent, relatively-simple learning processes, which
greatly reduces the model size. We evaluate the proposed
framework using the publicly available JIGSAWS robotic
surgery dataset and demonstrate its capability to learn the
underlying features of surgical maneuvers and the dynamic
interplay between sequences of actions effectively. The skill
level classification accuracy of 97:27% on the public dataset
demonstrates the superiority of the proposed model over prior
video-based skill assessment methods. -
- Date created
- 2021-01-01
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- Subjects / Keywords
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- Type of Item
- Conference/Workshop Presentation