Quantitative Assessment of the Restoration of Original Anatomy after 3D Virtual Reduction of Long Bone Fractures

Abstract

Background: The purpose of this study was to demonstrate the usefulness of 3D image-based virtual reduction by validating the evaluation criteria according to guidelines suggested by the AO Surgery Reference. Methods: For this experiment, 19 intact radial ORTHObones (ORTHObones radius, 3B Scientific, Germany, Hamburg) without any fractures were prepared. All ORTHObones with six cortical marking holes (three points on the distal part and three points on the proximal part) were scanned using a CT scanner twice (before/after intentional fracture of the ORTHObone). After the virtual reduction of all 19 ORTHObones, accuracy evaluations using the four criteria (length variation, apposition variation, alignment variation, Rotation Variation) suggested in the AO Surgery Reference were performed. Results: The mean (M) length variation was 0.42 mm, with 0.01 mm standard deviation (SD). The M apposition variation was 0.48 mm, with 0.40 mm SD. The M AP angulation variation (for alignment variation) was 3.24°, with 2.95° SD. The M lateral angulation variation (for alignment variation) was 0.09°, with 0.13° SD. The M angle of axial rotation was 1.27° with SD: 1.19°. Conclusions: The method of accuracy evaluation used in this study can be helpful in establishing a reliable plan.

Biomechanical and clinical evaluation of interlocking hip screw in Pauwels Ⅲ femoral neck fractures: A comparison with inverted triangle cannulated screws

“…Twenty composite femurs ( Kim et al, 2022 ) (#1005117, ORTHObones, 3B Scientific, Hamburg, Germany) were randomly assigned to two groups: Group IHS ( n = 10) fixed with IHS (Waston Medical, Changzhou, Jiangsu, China) and Group ITCS ( n = 10) fixed with ITCS (Waston Medical, Changzhou, Jiangsu, China). Before osteotomy, all specimens were predrilled for subsequent anatomical reduction.…”

Automatic segmentation of inconstant fractured fragments for tibia/fibula from CT images using deep learning

“…Segmented bone areas from computed tomography (CT) images can be used to fabricate patient-specific instruments or simulate surgical processes 40 . However, segmented images are typically acquired manually or interactively using a dedicated tool [40][41][42] . Unfortunately, medical centers still perform image segmentation using these methods when required.…”

“…Unfortunately, medical centers still perform image segmentation using these methods when required. In this case, successful image segmentation is time-consuming and requires trained practitioners [40][41][42] . Therefore, the effectiveness and usefulness of semantic segmentation are remarkable 25 .…”

“…Orthopedic trauma includes several fractures with various patterns and conditions. In particular, a highly complex comminuted fracture makes it difficult for surgeons to perform reduction and the operation is timeconsuming [41][42][43][44][45][46] . Therefore, surgeons typically want to correctly identify bone fragments before surgery using 2D/3D CT images 41,43 .…”

“…In particular, a highly complex comminuted fracture makes it difficult for surgeons to perform reduction and the operation is timeconsuming [41][42][43][44][45][46] . Therefore, surgeons typically want to correctly identify bone fragments before surgery using 2D/3D CT images 41,43 . However, because it is difficult for surgeons are mostly hard to identify all fragments of the bone by comminuted fractures using only CT images and one color, they find unidentified fragments during the operation with an open approach 41 .…”