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- W2945292029 abstract "Currently, the assessment of surgical skills relies primarily on the observations of expert surgeons. This may be time-consuming, non-scalable, inconsistent and subjective. Therefore, an automated system that can objectively identify the actual skills level of a junior trainee is highly desirable. This study aims to design an automated surgical skills evaluation system. We propose to use a deep neural network model that can analyze raw surgical motion data with minimal preprocessing. A platform with inertial measurement unit sensors was developed and participants with different levels of surgical experience were recruited to perform core open surgical skills tasks. JIGSAWS a publicly available robot based surgical training dataset was used to evaluate the generalization of our deep network model. 15 participants (4 experts, 4 intermediates and 7 novices) were recruited into the study. The proposed deep model achieved an accuracy of 98.2%. With comparison to JIGSAWS; our method outperformed some existing approaches with an accuracy of 98.4%, 98.4% and 94.7% for suturing, needle-passing, and knot-tying, respectively. The experimental results demonstrated the applicability of this method in both open surgery and robot-assisted minimally invasive surgery. This study demonstrated the potential ability of the proposed deep network model to learn the discriminative features between different surgical skills levels." @default.
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- W2945292029 date "2019-08-01" @default.
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- W2945292029 title "Surgical skill levels: Classification and analysis using deep neural network model and motion signals" @default.
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- W2945292029 doi "https://doi.org/10.1016/j.cmpb.2019.05.008" @default.
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