Surgical skill assessment using motion texture analysis

Sharma, Yachna

Title:

Surgical skill assessment using motion texture analysis

dc.contributor.advisor	Clements, Mark A.
dc.contributor.author	Sharma, Yachna
dc.contributor.committeeMember	Essa, Irfan
dc.contributor.committeeMember	Anderson, David
dc.contributor.committeeMember	Yezzi, Anthony
dc.contributor.committeeMember	Barnes, Christopher F.
dc.contributor.committeeMember	Ploetz, Thomas
dc.contributor.committeeMember	Sarin, Eric L.
dc.contributor.department	Electrical and Computer Engineering
dc.date.accessioned	2014-05-22T15:33:35Z
dc.date.available	2014-05-22T15:33:35Z
dc.date.created	2014-05
dc.date.issued	2014-04-07
dc.date.submitted	May 2014
dc.date.updated	2014-05-22T15:33:35Z
dc.description.abstract	In 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.description.degree	Ph.D.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/51890
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Surgery
dc.subject	Skill
dc.subject	Classification
dc.subject	Prediction
dc.subject	Motion texture
dc.subject.lcsh	Surgeons Rating of
dc.subject.lcsh	Motion
dc.subject.lcsh	Motor ability
dc.subject.lcsh	Tactile sensors
dc.subject.lcsh	Algorithms
dc.title	Surgical skill assessment using motion texture analysis
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.advisor	Clements, Mark A.
local.contributor.corporatename	School of Electrical and Computer Engineering
local.contributor.corporatename	College of Engineering
relation.isAdvisorOfPublication	3aab233d-d0e3-4bb3-9a2b-15a71b6d29d5
relation.isOrgUnitOfPublication	5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication	7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level	Doctoral