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dc.contributor.authorSharma, Yachna
dc.contributor.authorPlötz, Thomas
dc.contributor.authorHammerla, Nils
dc.contributor.authorMello, Sebastian
dc.contributor.authorMcNaney, Roisin
dc.contributor.authorOlivier, Patrick
dc.contributor.authorDeshmukh, Sandeep
dc.contributor.authorMcCaskie, Andrew
dc.contributor.authorEssa, Irfan
dc.date.accessioned2015-06-17T18:01:55Z
dc.date.available2015-06-17T18:01:55Z
dc.date.issued2014-04
dc.identifier.citationY. Sharma, T. Ploetz, N. Hammerla, S. Mellor, R. McNaney, P. 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.en_US
dc.identifier.urihttp://hdl.handle.net/1853/53658
dc.description© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en_US
dc.descriptionDOI: 10.1109/ISBI.2014.6867908
dc.description.abstractThe 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.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectMotion textureen_US
dc.subjectObjective structured assessment of technical skillsen_US
dc.subjectOSATSen_US
dc.subjectSurgical skillen_US
dc.subjectVideo analysisen_US
dc.titleAutomated surgical OSATS prediction from videosen_US
dc.typeProceedingsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Robotics and Intelligent Machinesen_US
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computingen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Interactive Computingen_US
dc.contributor.corporatenameNewcastle Universityen_US
dc.contributor.corporatenameNewcastle University. School of Computing Scienceen_US
dc.publisher.originalInstitute of Electrical and Electronics Engineers
dc.identifier.doi10.1109/ISBI.2014.6867908
dc.embargo.termsnullen_US


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