A Domain-Adapted Machine Learning Approach for Visual Evaluation and Interpretation of Robot-Assisted Surgery Skills
| dc.contributor.author | Abed Soleymani | |
| dc.contributor.author | Xingyu Li | |
| dc.contributor.author | Mahdi Tavakoli | |
| dc.date.accessioned | 2025-05-01T12:15:49Z | |
| dc.date.available | 2025-05-01T12:15:49Z | |
| dc.date.issued | 2022-01-01 | |
| dc.description | 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. | |
| dc.identifier.doi | https://doi.org/10.7939/r3-m2x2-8b41 | |
| dc.language.iso | en | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | |
| dc.subject | Machine Learning, Surgical Skills Evaluation, Ensemble Models, Contrastive Principal Component Analysis (cPCA), t-distributed Stochastic Neighbor Embedding (t-SNE) | |
| dc.title | A Domain-Adapted Machine Learning Approach for Visual Evaluation and Interpretation of Robot-Assisted Surgery Skills | |
| dc.type | http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| ual.jupiterAccess | http://terms.library.ualberta.ca/public |
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