HapticV1, PascalFrancis, Checkpoint, bibRecord, 000333

Learning Retention of Thoracic Pedicle Screw Placement Using a High-Resolution Augmented Reality Simulator With Haptic Feedback

Identifieur interne : 000333 ( PascalFrancis/Checkpoint ); précédent : 000332; suivant : 000334

Learning Retention of Thoracic Pedicle Screw Placement Using a High-Resolution Augmented Reality Simulator With Haptic Feedback

Auteurs : Cristian J. Luciano [États-Unis] ; P. Pat Banerjee [États-Unis] ; Brad Bellotte [États-Unis] ; G. Michael [États-Unis] ; Michael Jr Lemole [États-Unis] ; Fady T. Charbel [États-Unis] ; Ben Roitberg [États-Unis]

Source :

Neurosurgery [ 0148-396X ] ; 2011.

RBID : Pascal:11-0381804

Descripteurs français

Pascal (Inist)
- Pathologie du système nerveux, Apprentissage, Vis, Haute résolution, Réalité augmentée, Simulateur, Boucle réaction, Chirurgie, Simulation, Réalité virtuelle.
Wicri :
- topic : Chirurgie, Simulation, Réalité virtuelle.

English descriptors

KwdEn :
- Augmented reality, Feedback, High resolution, Learning, Nervous system diseases, Screw, Simulation, Simulator, Surgery, Virtual reality.

Abstract

BACKGROUND: 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. OBJECTIVE: To evaluate the learning retention of thoracic pedicle screw placement on a high-performance augmented reality and haptic technology workstation. METHODS: 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. RESULTS: With a 12.5% failure rate, a 2-proportion ztest yielded P=.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 = .04 from a 2-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. CONCLUSION: 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 = .08) toward learning retention resulting in improvement from practice to test sessions. The performance accuracy showed a 15% mean score improvement and more than a 50% reduction in standard deviation from practice to test. It showed evidence (P = .04) of performance accuracy improvement from practice to test session.

Affiliations:

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Le document en format XML

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<front><div type="abstract" xml:lang="en">BACKGROUND: 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. OBJECTIVE: To evaluate the learning retention of thoracic pedicle screw placement on a high-performance augmented reality and haptic technology workstation. METHODS: 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. RESULTS: With a 12.5% failure rate, a 2-proportion ztest yielded P=.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 = .04 from a 2-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. CONCLUSION: 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 = .08) toward learning retention resulting in improvement from practice to test sessions. The performance accuracy showed a 15% mean score improvement and more than a 50% reduction in standard deviation from practice to test. It showed evidence (P = .04) of performance accuracy improvement from practice to test session.</div>
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<fC01 i1="01" l="ENG"><s0>BACKGROUND: 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. OBJECTIVE: To evaluate the learning retention of thoracic pedicle screw placement on a high-performance augmented reality and haptic technology workstation. METHODS: 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. RESULTS: With a 12.5% failure rate, a 2-proportion ztest yielded P=.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 = .04 from a 2-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. CONCLUSION: 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 = .08) toward learning retention resulting in improvement from practice to test sessions. The performance accuracy showed a 15% mean score improvement and more than a 50% reduction in standard deviation from practice to test. It showed evidence (P = .04) of performance accuracy improvement from practice to test session.</s0>
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<s5>14</s5>
</fC03>
<fC03 i1="07" i2="X" l="SPA"><s0>Retroalimentación</s0>
<s5>14</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Chirurgie</s0>
<s5>15</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Surgery</s0>
<s5>15</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Cirugía</s0>
<s5>15</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE"><s0>Simulation</s0>
<s5>17</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG"><s0>Simulation</s0>
<s5>17</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Simulación</s0>
<s5>17</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE"><s0>Réalité virtuelle</s0>
<s5>18</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG"><s0>Virtual reality</s0>
<s5>18</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA"><s0>Realidad virtual</s0>
<s5>18</s5>
</fC03>
<fN21><s1>262</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
</standard>
</inist>
<affiliations><list><country><li>États-Unis</li>
</country>
<region><li>Arizona</li>
<li>Illinois</li>
<li>Pennsylvanie</li>
</region>
</list>
<tree><country name="États-Unis"><region name="Illinois"><name sortKey="Luciano, Cristian J" sort="Luciano, Cristian J" uniqKey="Luciano C" first="Cristian J." last="Luciano">Cristian J. Luciano</name>
</region>
<name sortKey="Banerjee, P Pat" sort="Banerjee, P Pat" uniqKey="Banerjee P" first="P. Pat" last="Banerjee">P. Pat Banerjee</name>
<name sortKey="Banerjee, P Pat" sort="Banerjee, P Pat" uniqKey="Banerjee P" first="P. Pat" last="Banerjee">P. Pat Banerjee</name>
<name sortKey="Banerjee, P Pat" sort="Banerjee, P Pat" uniqKey="Banerjee P" first="P. Pat" last="Banerjee">P. Pat Banerjee</name>
<name sortKey="Bellotte, Brad" sort="Bellotte, Brad" uniqKey="Bellotte B" first="Brad" last="Bellotte">Brad Bellotte</name>
<name sortKey="Charbel, Fady T" sort="Charbel, Fady T" uniqKey="Charbel F" first="Fady T." last="Charbel">Fady T. Charbel</name>
<name sortKey="Lemole, Michael Jr" sort="Lemole, Michael Jr" uniqKey="Lemole M" first="Michael Jr" last="Lemole">Michael Jr Lemole</name>
<name sortKey="Luciano, Cristian J" sort="Luciano, Cristian J" uniqKey="Luciano C" first="Cristian J." last="Luciano">Cristian J. Luciano</name>
<name sortKey="Michael, G" sort="Michael, G" uniqKey="Michael G" first="G." last="Michael">G. Michael</name>
<name sortKey="Roitberg, Ben" sort="Roitberg, Ben" uniqKey="Roitberg B" first="Ben" last="Roitberg">Ben Roitberg</name>
</country>
</tree>
</affiliations>
</record>

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Learning Retention of Thoracic Pedicle Screw Placement Using a High-Resolution Augmented Reality Simulator With Haptic Feedback

Learning Retention of Thoracic Pedicle Screw Placement Using a High-Resolution Augmented Reality Simulator With Haptic Feedback

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