Linked Life Data

17127647

Source:http://linkedlifedata.com/resource/pubmed/id/17127647

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2006-11-27
pubmed:abstractText
This paper reports our progress in developing techniques for "parsing" raw motion data from a simple surgical task into a labeled sequence of surgical gestures. The ability to automatically detect and segment surgical motion can be useful in evaluating surgical skill, providing surgical training feedback, or documenting essential aspects of a procedure. If processed online, the information can be used to provide context-specific information or motion enhancements to the surgeon. However, in every case, the key step is to relate recorded motion data to a model of the procedure being performed. Robotic surgical systems such as the da Vinci system from Intuitive Surgical provide a rich source of motion and video data from surgical procedures. The application programming interface (API) of the da Vinci outputs 192 kinematics values at 10 Hz. Through a series of feature-processing steps, tailored to this task, the highly redundant features are projected to a compact and discriminative space. The resulting classifier is simple and effective.Cross-validation experiments show that the proposed approach can achieve accuracies higher than 90% when segmenting gestures in a 4-throw suturing task, for both expert and intermediate surgeons. These preliminary results suggest that gesture-specific features can be extracted to provide highly accurate surgical skill evaluation.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
1092-9088
pubmed:author
pubmed:issnType
Print
pubmed:volume
11
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
220-30
pubmed:dateRevised
2009-11-3
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Towards automatic skill evaluation: detection and segmentation of robot-assisted surgical motions.
pubmed:affiliation
Engineering Research Center for Computer-Integrated Surgical Systems and Technology, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA. hcl@cs.jhu.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't