naa a22 4500 510808190 CHVBK 20180411083428.0 cr unu---uuuuu 180411e20130901xx s 000 0 eng 10.1007/s11548-012-0811-5 doi (NATIONALLICENCE)springer-10.1007/s11548-012-0811-5 Ultrasound-guided facet joint injection training using Perk Tutor [Elektronische Daten] [E. Moult, T. Ungi, M. Welch, J. Lu, R. McGraw, G. Fichtinger] Purpose: Facet syndrome is a condition that may cause 15-45% of chronic lower back pain. It is commonly diagnosed and treated using facet joint injections. This needle technique demands high accuracy, and ultrasound (US) is a potentially useful modality to guide the needle. US-guided injections, however, require physicians to interpret 2-D sonographic images while simultaneously manipulating an US probe and needle. Therefore, US-guidance for facet joint injections needs advanced training methodologies that will equip physicians with the requisite skills. Methods: We used Perk Tutor—an augmented reality training system for US-guided needle insertions—in a configuration for percutaneous procedures of the lumbar spine. In a pilot study of 26 pre-medical undergraduate students, we evaluated the efficacy of Perk Tutor training compared to traditional training. Results: The Perk Tutor Trained group, which had access to Perk Tutor during training, had a mean success rate of 61.5%, while the Control group, which received traditional training, had a mean success rate of 38.5% ( $$p = 0.031$$ ). No significant differences in procedure times or needle path lengths were observed between the two groups. Conclusions: The results of this pilot study suggest that Perk Tutor provides an improved training environment for US-guided facet joint injections on a synthetic model. CARS, 2013 Facet joint injection nationallicence Ultrasound-guidance nationallicence Medical education nationallicence Simulation training nationallicence Perk Tutor nationallicence Moult E. Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada aut Ungi T. School of Computing, Queen's University, Kingston, ON, Canada aut Welch M. School of Computing, Queen's University, Kingston, ON, Canada aut Lu J. School of Computing, Queen's University, Kingston, ON, Canada aut McGraw R. School of Medicine, Queen's University, Kingston, ON, Canada aut Fichtinger G. School of Computing, Queen's University, Kingston, ON, Canada aut International Journal of Computer Assisted Radiology and Surgery Springer Berlin Heidelberg 8/5(2013-09-01), 831-836 1861-6410 8:5<831 2013 8 11548 https://doi.org/10.1007/s11548-012-0811-5 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11548-012-0811-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Moult E. Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada aut NATIONALLICENCE 700 1- Ungi T. School of Computing, Queen's University, Kingston, ON, Canada aut NATIONALLICENCE 700 1- Welch M. School of Computing, Queen's University, Kingston, ON, Canada aut NATIONALLICENCE 700 1- Lu J. School of Computing, Queen's University, Kingston, ON, Canada aut NATIONALLICENCE 700 1- McGraw R. School of Medicine, Queen's University, Kingston, ON, Canada aut NATIONALLICENCE 700 1- Fichtinger G. School of Computing, Queen's University, Kingston, ON, Canada aut NATIONALLICENCE 773 0- International Journal of Computer Assisted Radiology and Surgery Springer Berlin Heidelberg 8/5(2013-09-01), 831-836 1861-6410 8:5<831 2013 8 11548 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer