A Domain-Adapted Machine Learning Approach for Visual Evaluation and Interpretation of Robot-Assisted Surgery Skills
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In this study, we present an intuitive machine learningbased
approach to evaluate and interpret surgical skills level of a
participant working with robotic platforms. The proposed method is
domain-adapted, i.e., jointly utilizes an end-to-end learning approach
for smoothness detection and domain knowledge-based metrics such
as fluidity and economy of motion for extracting skills-related features
within a given trajectory. An advantage of our approach compared
to similar stochastic or deep learning models is its intuitive and
transparent manner for extraction and visualization of skills-related
features within the data. We illustrate the performance of our
proposed method on trials of the JIGSAWS data set as well as our
own experimental data gathered from Phantom Premium 1.5A
Haptic Device. This approach utilized t-SNE technique and provides
visualized low-dimensional representation for different trials that
highlights nuanced information within the executive task and returns
unusual or faulty trials as outliers far away from their normal skill
or participant clusters. This information regarding the input trajectory
can be used for evaluation and education applications such as learning
curve analysis in surgical assessment and training programs.
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http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/version/c_970fb48d4fbd8a85
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en