Lars Brouwers1,2, Albert F Pull Ter Gunne3, Mariska A de Jongh4,5, Thomas J J Maal6, Rinaldo Vreeken6, Frank H W M van der Heijden7, Luke P H Leenen8, Willem R Spanjersberg9, Sven H van Helden9, Diederik O Verbeek10, Mike Bemelman7, Koen W W Lansink4,5,7. 1. Brabant Trauma Registry, Network Emergency Care Brabant, Elisabeth-Tweesteden Hospital, Tilburg, Noord-Brabant, The Netherlands. l.brouwers@etz.nl. 2. Department of Surgery, RadboudUMC, Nijmegen, Gelderland, The Netherlands. l.brouwers@etz.nl. 3. Department of Surgery, Rijnstate Hospital, Arnhem, Gelderland, The Netherlands. 4. Brabant Trauma Registry, Network Emergency Care Brabant, Elisabeth-Tweesteden Hospital, Tilburg, Noord-Brabant, The Netherlands. 5. Department of Surgery, RadboudUMC, Nijmegen, Gelderland, The Netherlands. 6. 3D Lab, Department of Oral- and Maxillofacial Surgery, RadboudUMC, Nijmegen, Gelderland, The Netherlands. 7. Department of Surgery, Elisabeth-Tweesteden Hospital, Tilburg, Noord-Brabant, The Netherlands. 8. Department of Surgery, University Medical Center Utrecht, Utrecht, State Utrecht, The Netherlands. 9. Department of Surgery, Isala Hospital, Zwolle, Overijsel, The Netherlands. 10. Department of Surgery, Erasmus University Medical Center, Rotterdam, Zuid-Holland, The Netherlands.
Abstract
BACKGROUND: Acetabular fractures are difficult to classify owing to the complex three-dimensional (3D) anatomy of the pelvis. 3D printing helps to understand and reliably classify acetabular fracture types. 3D-virtual reality (VR) may have comparable benefits. Our hypothesis is that 3D-VR is equivalent to 3D printing in understanding acetabular fracture patterns. METHODS: A total of 27 observers of various experience levels from several hospitals were requested to classify twenty 3D printed and VR models according to the Judet-Letournel classification. Additionally, surgeons were asked to state their preferred surgical approach and patient positioning. Time to classify each fracture type was recorded. The cases were randomized to rule out a learning curve. Inter-observer agreement was analyzed using Fleiss' kappa statistics (κ). RESULTS: Inter-observer agreements varied by observer group and type of model used to classify the fracture: medical students: 3D print (κ = 0.61), VR (κ = 0.41); junior surgical residents: 3D print (0.51) VR (0.54); senior surgical residents: 3D print (0.66) VR (0.52); junior surgeons: 3D print (0.56), VR (0.43); senior surgeons: 3D print (κ = 0.59), VR (κ = 0.42). Using 3D printed models, there was more agreement on the surgical approach (junior surgeons κ = 0.23, senior surgeons κ = 0.31) when compared with VR (junior surgeons κ = 0.17, senior surgeons 0.25). No difference was found in time used to classify these fractures between 3D printing and VR for all groups (P = 1.000). CONCLUSIONS: The Judet-Letournel acetabular classification stays difficult to interpret; only moderate kappa agreements were found. We found 3D-VR inferior to 3D printing in classifying acetabular fractures. Furthermore, the current 3D-VR technology is still not practical for intra-operative use.
BACKGROUND: Acetabular fractures are difficult to classify owing to the complex three-dimensional (3D) anatomy of the pelvis. 3D printing helps to understand and reliably classify acetabular fracture types. 3D-virtual reality (VR) may have comparable benefits. Our hypothesis is that 3D-VR is equivalent to 3D printing in understanding acetabular fracture patterns. METHODS: A total of 27 observers of various experience levels from several hospitals were requested to classify twenty 3D printed and VR models according to the Judet-Letournel classification. Additionally, surgeons were asked to state their preferred surgical approach and patient positioning. Time to classify each fracture type was recorded. The cases were randomized to rule out a learning curve. Inter-observer agreement was analyzed using Fleiss' kappa statistics (κ). RESULTS: Inter-observer agreements varied by observer group and type of model used to classify the fracture: medical students: 3D print (κ = 0.61), VR (κ = 0.41); junior surgical residents: 3D print (0.51) VR (0.54); senior surgical residents: 3D print (0.66) VR (0.52); junior surgeons: 3D print (0.56), VR (0.43); senior surgeons: 3D print (κ = 0.59), VR (κ = 0.42). Using 3D printed models, there was more agreement on the surgical approach (junior surgeons κ = 0.23, senior surgeons κ = 0.31) when compared with VR (junior surgeons κ = 0.17, senior surgeons 0.25). No difference was found in time used to classify these fractures between 3D printing and VR for all groups (P = 1.000). CONCLUSIONS: The Judet-Letournel acetabular classification stays difficult to interpret; only moderate kappa agreements were found. We found 3D-VR inferior to 3D printing in classifying acetabular fractures. Furthermore, the current 3D-VR technology is still not practical for intra-operative use.
Entities:
Keywords:
3D printing; Acetabular surgery; Inter-observer; Judet–Letournel classification; Virtual reality
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