Learning retention of thoracic pedicle screw placement using a high-resolution augmented reality simulator with haptic feedback1
Identifieur interne : 001985 ( Ncbi/Curation ); précédent : 001984; suivant : 001986Learning retention of thoracic pedicle screw placement using a high-resolution augmented reality simulator with haptic feedback1
Auteurs : Cristian J. Luciano ; P. Pat Banerjee ; Brad Bellotte [États-Unis] ; G. Michael Lemole [États-Unis] ; Michael Oh [États-Unis] ; Fady T. Charbel ; Ben RoitbergSource :
- Neurosurgery [ 0148-396X ] ; 2011.
English descriptors
- KwdEn :
- MESH :
- education : Neurosurgery.
- methods : Education, Medical, Graduate, Spinal Fusion.
- surgery : Thoracic Vertebrae.
- Bone Screws, Computer Simulation, Humans, Learning, Retention (Psychology), User-Computer Interface.
Abstract
We evaluated the use of a part-task simulator with 3D and haptic feedback as a training tool for a common neurosurgical procedure – placement of thoracic pedicle screws.
To evaluate the learning retention of thoracic pedicle screw placement on a high-performance augmented reality and haptic technology workstation.
Fifty-one fellows and residents performed thoracic pedicle screw placement on the simulator. The virtual screws were drilled into a virtual patient’s thoracic spine derived from a computed tomography data set of a real patient.
With a 12.5% failure rate, a two-proportion z-test yielded P= 0.08. For performance accuracy, an aggregate Euclidean distance deviation from entry landmark on the pedicle and a similar deviation from the target landmark in the vertebral body yielded P=0.04 from a two-sample t-test in which the rejected null hypothesis assumes no improvement in performance accuracy from the practice to the test sessions, and the alternative hypothesis assumes an improvement.
The performance accuracy on the simulator was comparable to the accuracy reported in literature on recent retrospective evaluation of such placements. The failure rates indicated a minor drop from practice to test sessions, and also indicated a trend (P=0.08) towards learning retention resulting in improvement from practice to test sessions. The performance accuracy showed a 15% mean score improvement and over 50% reduction in standard deviation from practice to test. It showed evidence (P=0.04) of performance accuracy improvement from practice to test session.
Url:
DOI: 10.1227/NEU.0b013e31821954ed
PubMed: 21471846
PubMed Central: 3153609
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Cristian J. Luciano<affiliation><nlm:aff id="A1"> Department of Mechanical and Industrial Engineering, College of Engineering, University of Illinois at Chicago</nlm:aff>
<wicri:noCountry code="subfield">University of Illinois at Chicago</wicri:noCountry>
</affiliation>
<affiliation><nlm:aff id="A3"> Department of Computer Science, College of Engineering, University of Illinois at Chicago</nlm:aff>
<wicri:noCountry code="subfield">University of Illinois at Chicago</wicri:noCountry>
</affiliation>
<affiliation><nlm:aff id="A3"> Department of Computer Science, College of Engineering, University of Illinois at Chicago</nlm:aff>
<wicri:noCountry code="subfield">University of Illinois at Chicago</wicri:noCountry>
</affiliation>
<affiliation><nlm:aff id="A1"> Department of Mechanical and Industrial Engineering, College of Engineering, University of Illinois at Chicago</nlm:aff>
<wicri:noCountry code="subfield">University of Illinois at Chicago</wicri:noCountry>
</affiliation>
<affiliation><nlm:aff id="A2"> Department of Bioengineering, College of Engineering, University of Illinois at Chicago</nlm:aff>
<wicri:noCountry code="subfield">University of Illinois at Chicago</wicri:noCountry>
</affiliation>
<affiliation><nlm:aff id="A3"> Department of Computer Science, College of Engineering, University of Illinois at Chicago</nlm:aff>
<wicri:noCountry code="subfield">University of Illinois at Chicago</wicri:noCountry>
</affiliation>
<affiliation><nlm:aff id="A2"> Department of Bioengineering, College of Engineering, University of Illinois at Chicago</nlm:aff>
<wicri:noCountry code="subfield">University of Illinois at Chicago</wicri:noCountry>
</affiliation>
<affiliation><nlm:aff id="A3"> Department of Computer Science, College of Engineering, University of Illinois at Chicago</nlm:aff>
<wicri:noCountry code="subfield">University of Illinois at Chicago</wicri:noCountry>
</affiliation>
<affiliation><nlm:aff id="A3"> Department of Computer Science, College of Engineering, University of Illinois at Chicago</nlm:aff>
<wicri:noCountry code="subfield">University of Illinois at Chicago</wicri:noCountry>
</affiliation>
<affiliation><nlm:aff id="A4"> Department of Neurosurgery, University of Illinois at Chicago</nlm:aff>
<wicri:noCountry code="subfield">University of Illinois at Chicago</wicri:noCountry>
</affiliation>
<affiliation><nlm:aff id="A6"> Divisionof Neurosurgery, University of Chicago</nlm:aff>
<wicri:noCountry code="subfield">University of Chicago</wicri:noCountry>
</affiliation>
Le document en format XML
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Learning retention of thoracic pedicle screw placement using a high-resolution augmented reality simulator with haptic feedback<xref rid="FN3" ref-type="fn">1</xref>
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<author><name sortKey="Luciano, Cristian J" sort="Luciano, Cristian J" uniqKey="Luciano C" first="Cristian J." last="Luciano">Cristian J. Luciano</name>
<affiliation><nlm:aff id="A1"> Department of Mechanical and Industrial Engineering, College of Engineering, University of Illinois at Chicago</nlm:aff>
<wicri:noCountry code="subfield">University of Illinois at Chicago</wicri:noCountry>
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<affiliation><nlm:aff id="A3"> Department of Computer Science, College of Engineering, University of Illinois at Chicago</nlm:aff>
<wicri:noCountry code="subfield">University of Illinois at Chicago</wicri:noCountry>
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<wicri:noCountry code="subfield">University of Illinois at Chicago</wicri:noCountry>
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<series><title level="j">Neurosurgery</title>
<idno type="ISSN">0148-396X</idno>
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<imprint><date when="2011">2011</date>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Bone Screws</term>
<term>Computer Simulation</term>
<term>Education, Medical, Graduate (methods)</term>
<term>Humans</term>
<term>Learning</term>
<term>Neurosurgery (education)</term>
<term>Retention (Psychology)</term>
<term>Spinal Fusion (methods)</term>
<term>Thoracic Vertebrae (surgery)</term>
<term>User-Computer Interface</term>
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<front><div type="abstract" xml:lang="en"><sec id="S1"><title>Background</title>
<p id="P1">We evaluated the use of a part-task simulator with 3D and haptic feedback as a training tool for a common neurosurgical procedure – placement of thoracic pedicle screws.</p>
</sec>
<sec id="S2"><title>Objective</title>
<p id="P2">To evaluate the learning retention of thoracic pedicle screw placement on a high-performance augmented reality and haptic technology workstation.</p>
</sec>
<sec sec-type="methods" id="S3"><title>Methods</title>
<p id="P3">Fifty-one fellows and residents performed thoracic pedicle screw placement on the simulator. The virtual screws were drilled into a virtual patient’s thoracic spine derived from a computed tomography data set of a real patient.</p>
</sec>
<sec id="S4"><title>Results</title>
<p id="P4">With a 12.5% failure rate, a two-proportion z-test yielded P= 0.08. For performance accuracy, an aggregate Euclidean distance deviation from entry landmark on the pedicle and a similar deviation from the target landmark in the vertebral body yielded P=0.04 from a two-sample t-test in which the rejected null hypothesis assumes no improvement in performance accuracy from the practice to the test sessions, and the alternative hypothesis assumes an improvement.</p>
</sec>
<sec id="S5"><title>Conclusion</title>
<p id="P5">The performance accuracy on the simulator was comparable to the accuracy reported in literature on recent retrospective evaluation of such placements. The failure rates indicated a minor drop from practice to test sessions, and also indicated a trend (P=0.08) towards learning retention resulting in improvement from practice to test sessions. The performance accuracy showed a 15% mean score improvement and over 50% reduction in standard deviation from practice to test. It showed evidence (P=0.04) of performance accuracy improvement from practice to test session.</p>
</sec>
</div>
</front>
</TEI>
</record>
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