Paper in IBSI 2014 conference entitled “Automated Surgical OSATS Prediction from Videos”

  • Y. Sharma, T. Ploetz, N. Hammerla, S. Mellor, R. McNaney, Patrick Oliver, S. Deshmukh, A. McCaskie, and I. Essa (2014), “Automated Surgical OSATS Prediction from Videos,” in Proceedings of IEEE International Symposium on Biomedical Imaging, Beijing, CHINA, 2014. [PDF] [BIBTEX]
    @InProceedings{ 2014-Sharma-ASOPFV,
    address  = {Beijing, CHINA},
    author  = {Yachna Sharma and Thomas Ploetz and Nils Hammerla
    and Sebastian Mellor and Roisin McNaney and Patrick
    Oliver and Sandeep Deshmukh and Andrew McCaskie and
    Irfan Essa},
    booktitle  = {{Proceedings of IEEE International Symposium on
    Biomedical Imaging}},
    month = {April},
    pdf = {},
    title = {Automated Surgical {OSATS} Prediction from Videos},
    year = {2014}


The assessment of surgical skills is an essential part of medical training. The prevalent manual evaluations by expert surgeons are time-consuming and often their outcomes vary substantially from one observer to another. We present a video-based framework for automated evaluation of surgical skills based on the Objective Structured Assessment of Technical Skills (OSATS) criteria. We encode the motion dynamics via frame kernel matrices and represent the motion granularity by texture features. Linear discriminant analysis is used to derive a reduced dimensionality feature space followed by linear regression to predict OSATS skill scores. We achieve a statistically significant correlation (p-value < 0.01) between the ground-truth (given by domain experts) and the OSATS scores predicted by our framework.

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