Automated surgical OSATS prediction from videos

Roisin McNaney, Yachna Sharma, Thomas Ploetz, Nils Hammerla, Sebastian Mellor, Patrick Olivier, Sandeep Deshmuhk, Andrew McCaskie, Irfan Essa

Research output: Contribution to conferenceConference Paperpeer-review


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 statistically significant correlation (p-value <;0.01) between the ground-truth (given by domain experts) and the OSATS scores predicted by our framework.
Original languageEnglish
Number of pages464
Publication statusPublished - 2014
EventIEEE 11th International Symposium on Biomedical Imaging: ISBI -
Duration: 3 Mar 2014 → …


ConferenceIEEE 11th International Symposium on Biomedical Imaging
Period3/03/14 → …

Structured keywords

  • Digital Health


  • Digital Health
  • Surgery


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