Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery.
Authors of this article are:
Wang Z Majewicz Fey A.
A summary of the article is shown below:
PURPOSE: With the advent of robot-assisted surgery, the role of data-driven approaches to integrate statistics and machine learning is growing rapidly with prominent interests in objective surgical skill assessment. However, most existing work requires translating robot motion kinematics into intermediate features or gesture segments that are expensive to extract, lack efficiency, and require significant domain-specific knowledge.METHODS: We propose an analytical deep learning framework for skill assessment in surgical training. A deep convolutional neural network is implemented to map multivariate time series data of the motion kinematics to individual skill levels.RESULTS: We perform experiments on the public minimally invasive surgical robotic dataset, JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). Our proposed learning model achieved competitive accuracies of 92.5%, 95.4%, and 91.3%, in the standard training tasks: Suturing, Needle-passing, and Knot-tying, respectively. Without the need of engineered features or carefully tuned gesture segmentation, our model can successfully decode skill information from raw motion profiles via end-to-end learning. Meanwhile, the proposed model is able to reliably interpret skills within a 1-3 second window, without needing an observation of entire training trial.CONCLUSION: This study highlights the potential of deep architectures for efficient online skill assessment in modern surgical training.
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This article is a good source of information and a good way to become familiar with topics such as: Convolutional neural network;Deep learning;Motion analysis;Surgical robotics;Surgical skill evaluation.
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