Enhancing orthopedic resident competency in calcaneal fracture evaluation with sanders classification: A comparative study of computed tomography, 3D printing, and virtual reality

Aim: The aim of this study is to investigate the impact of modern technological tools in the process of learning and interpreting the Sanders classification of calcaneal fractures. The contributions of tools such as Computed Tomography (CT), 3D (three-dimensional) printing, and Virtual Reality (VR) headsets to orthopedic and trauma education have been compared. The study aimed to determine the effectiveness of these methods in the classification of calcaneal fractures. Material and Method: Thirty-three patients diagnosed with calcaneal fractures and with available CT images were included in the study. Twenty-five orthopedic and trauma residents assessed each fracture sequentially using 2D CT slices, 3D prints, and VR images. The accuracy of the classifications made using each method was analyzed through intra-observer and inter-observer agreement. Three different assessment sessions were organized, and results were recorded using a different method in each session. Results: In terms of inter-observer agreement, the 3D printing method yielded the most successful results. In terms of accuracy, the CT-based evaluations achieved the highest correct response rate. The most preferred method was VR. No significant difference was found in classification success based on the year of residency. Conclusion: This study demonstrates that modern 3D technologies enhance inter-observer agreement compared to CT methods in calcaneal fracture classification. However, CT remains the most effective method in terms of accuracy. The study emphasizes the potential of integrating 3D technologies into orthopedic education to enhance resident competency and improve learning outcomes. Keywords: Calcaneal Fractures, Fracture Classification, Computed Tomography, Three-Dimensional Printing, Virtual Reality This study was funded by Scientific Research Projects Coordination Unit of Istanbul University-Cerrahpasa. Project ID: 37995. Project Code: TTU-2024-37995