Computer-assisted surgery in acetabular fractures: Virtual reduction of acetabular fracture using the first patient-specific biomechanical model simulator.

Preoperative planning for the management of acetabular fracture is founded on geometric models allowing virtual repositioning of the bone fragments, but not taking account of soft tissue and the realities of the surgical procedure. The present technical note reports results using the first simulator to be based on a patient-specific biomechanical model, simulating the action of forces on the fragments and also the interactions between soft issue and bone: muscles, capsules, ligaments, and bone contacts. In all 14 cases, biomechanical simulation faithfully reproduced the intraoperative behavior of the various bone fragments and reduction quality. On Matta's criteria, anatomic reduction was achieved in 12 of the 14 patients (86%; 0.25mm±0.45 [range: 0-1]) and in the 12 corresponding simulations (86%; 0.42mm±0.51 [range: 0-1]). Mean semi-automatic segmentation time was 156min±37.9 [range: 120-180]. Mean simulation time was 23min±9 [range: 16-38]. The model needs larger-scale prospective validation, but offers a new tool suitable for teaching purposes and for assessment of surgical results in acetabular fracture. LEVEL OF EVIDENCE: IV: retrospective study.

Does semi-automatic bone-fragment segmentation improve the reproducibility of the Letournel acetabular fracture classification?

BACKGROUND: The Letournel classification of acetabular fracture shows poor reproducibility in inexperienced observers, despite the introduction of 3D imaging. We therefore developed a method of semi-automatic segmentation based on CT data. The present prospective study aimed to assess: (1) whether semi-automatic bone-fragment segmentation increased the rate of correct classification; (2) if so, in which fracture types; and (3) feasibility using the open-source itksnap 3.0 software package without incurring extra cost for users. HYPOTHESIS: Semi-automatic segmentation of acetabular fractures significantly increases the rate of correct classification by orthopedic surgery residents. METHODS: Twelve orthopedic surgery residents classified 23 acetabular fractures. Six used conventional 3D reconstructions provided by the center's radiology department (conventional group) and 6 others used reconstructions obtained by semi-automatic segmentation using the open-source itksnap 3.0 software package (segmentation group). Bone fragments were identified by specific colors. Correct classification rates were compared between groups on Chi2 test. Assessment was repeated 2 weeks later, to determine intra-observer reproducibility. RESULTS: Correct classification rates were significantly higher in the "segmentation" group: 114/138 (83%) versus 71/138 (52%); P<0.0001. The difference was greater for simple (36/36 (100%) versus 17/36 (47%); P<0.0001) than complex fractures (79/102 (77%) versus 54/102 (53%); P=0.0004). Mean segmentation time per fracture was 27±3min [range, 21-35min]. The segmentation group showed excellent intra-observer correlation coefficients, overall (ICC=0.88), and for simple (ICC=0.92) and complex fractures (ICC=0.84). CONCLUSION: Semi-automatic segmentation, identifying the various bone fragments, was effective in increasing the rate of correct acetabular fracture classification on the Letournel system by orthopedic surgery residents. It may be considered for routine use in education and training. LEVEL OF EVIDENCE: III: prospective case-control study of a diagnostic procedure.