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dc.contributor.advisorClements, Mark A.
dc.contributor.authorSharma, Yachna
dc.date.accessioned2014-05-22T15:33:35Z
dc.date.available2014-05-22T15:33:35Z
dc.date.created2014-05
dc.date.issued2014-04-07
dc.date.submittedMay 2014
dc.identifier.urihttp://hdl.handle.net/1853/51890
dc.description.abstractIn this thesis, we propose a framework for automated assessment of surgical skills to expedite the manual assessment process and to provide unbiased evaluations with possible dexterity feedback. Evaluation of surgical skills is an important aspect in training of medical students. Current practices rely on manual evaluations from faculty and residents and are time consuming. Proposed solutions in literature involve retrospective evaluations such as watching the offline videos. It requires precious time and attention of expert surgeons and may vary from one surgeon to another. With recent advancements in computer vision and machine learning techniques, the retrospective video evaluation can be best delegated to the computer algorithms. Skill assessment is a challenging task requiring expert domain knowledge that may be difficult to translate into algorithms. To emulate this human observation process, an appropriate data collection mechanism is required to track motion of the surgeon's hand in an unrestricted manner. In addition, it is essential to identify skill defining motion dynamics and skill relevant hand locations. This Ph.D. research aims to address the limitations of manual skill assessment by developing an automated motion analysis framework. Specifically, we propose (1) to design and implement quantitative features to capture fine motion details from surgical video data, (2) to identify and test the efficacy of a core subset of features in classifying the surgical students into different expertise levels, (3) to derive absolute skill scores using regression methods and (4) to perform dexterity analysis using motion data from different hand locations.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectSurgery
dc.subjectSkill
dc.subjectClassification
dc.subjectPrediction
dc.subjectMotion texture
dc.subject.lcshSurgeons Rating of
dc.subject.lcshMotion
dc.subject.lcshMotor ability
dc.subject.lcshTactile sensors
dc.subject.lcshAlgorithms
dc.titleSurgical skill assessment using motion texture analysis
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentElectrical and Computer Engineering
thesis.degree.levelDoctoral
dc.contributor.committeeMemberEssa, Irfan
dc.contributor.committeeMemberAnderson, David
dc.contributor.committeeMemberYezzi, Anthony
dc.contributor.committeeMemberBarnes, Christopher F.
dc.contributor.committeeMemberPloetz, Thomas
dc.contributor.committeeMemberSarin, Eric L.
dc.date.updated2014-05-22T15:33:35Z


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