Automated surgical OSATS prediction from videos

Yachna Sharma, Thomas Plötz, Nils Hammerld, Sebastian Mellor, Roisin McNaney, Patrick Olivier, Sandeep Deshmukh, Andrew McCaskie, Irfan Essa

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

22 Citations (Scopus)


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
Title of host publication2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014)
Subtitle of host publicationBeijing, China 29 April – 2 May 2014
EditorsLaure Blanc-Féraud, Jie Tian
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)9781467319614, 9781467319591
ISBN (Print)9781467319607
Publication statusPublished - 2014
Externally publishedYes
EventIEEE International Symposium on Biomedical Imaging (ISBI) 2014 - Beijing, China
Duration: 29 Apr 20142 May 2014
Conference number: 11th (Proceedings)


ConferenceIEEE International Symposium on Biomedical Imaging (ISBI) 2014
Abbreviated titleISBI 2014
Internet address


  • Motion texture
  • Surgical skill
  • Video analysis

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