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

Clinical validation of artificial intelligence-based preoperative virtual reduction for Neer 3- or 4-part proximal humerus fractures

BACKGROUND

If reduction images of fractures can be provided in advance with artificial-intelligence (AI)-based technology, it can assist with preoperative surgical planning. Recently, we developed the AI-based preoperative virtual reduction model for orthopedic trauma, which can provide an automatic segmentation and reduction of fractured fragments. The purpose of this study was to validate a quality of reduction model of Neer 3- or 4-part proximal humerus fractures established by AI-based technology.

METHODS

To develop the AI-based preoperative virtual reduction model, deep learning performed the segmentation of fracture fragments, and a Monte Carlo simulation completed the virtual reduction to determine the best model. A total of 20 pre/postoperative three-dimensional computed tomography (CT) scans of proximal humerus fracture were prepared. The preoperative CT scans were employed as the input of AI-based automated reduction (AI-R) to deduce the reduction models of fracture fragments, meanwhile, the manual reduction (MR) was conducted using the same CT images. Dice similarity coefficient (DSC) and intersection over union (IoU) between the reduction model from the AI-R/MR and postoperative CT scans were evaluated. Working times were compared between the two groups. Clinical validity agreement (CVA) and reduction quality score (RQS) were investigated for clinical validation outcomes by 20 orthopedic surgeons.

RESULTS

The mean DSC and IoU were better when using AI-R that when using MR (0.78 ± 0.13 vs. 0.69 ± 0.16, p < 0.001 and 0.65 ± 0.16 vs. 0.55 ± 0.18, p < 0.001, respectively). The working time of AI-R was, on average, 1.41% of that of MR. The mean CVA of all cases was 81%±14.7% (AI-R, 82.25%±14.27%; MR, 76.75%±14.17%, p = 0.06). The mean RQS was significantly higher when AI-R compared with MR was used (91.47 ± 1.12 vs. 89.30 ± 1.62, p = 0.045).

CONCLUSION

The AI-based preoperative virtual reduction model showed good performance in the reduction model in proximal humerus fractures with faster working times. Beyond diagnosis, classification, and outcome prediction, the AI-based technology can change the paradigm of preoperative surgical planning in orthopedic surgery.

LEVEL OF EVIDENCE

Level IV.

Combining robotics and 3D printing facilitates closed reduction of humeral shaft fractures using a minimally invasive plate as a reduction template: A proof-of-concept study

BACKGROUND

Minimally invasive percutaneous plate osteosynthesis for humeral shaft fractures (HSFs) has limitations due to malreduction and radiation exposure. To address these limitations, we integrated robotics and 3D printing by incorporating plates as reduction templates.

METHOD

The innovative technology facilitated closed reduction of HSFs in the operating theatre using 18 models with cortical marking holes. The dataset of the precontoured plate was imported into 3D planning software for virtual fixation and screw path planning. The models were divided into half to simulate transverse fractures. During the operation, the software generated drilling trajectories for robot navigation, and precise plate installation achieved automatic fracture reduction.

RESULTS

The evaluation results of reduction accuracy revealed variations in length, apposition, alignment, and rotation that meet the criteria for anatomic reduction. High interoperator reliabilities were observed for all parameters.

CONCLUSIONS

The proposed technology achieved anatomic reduction in simulated bones.

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

Orthopaedic surgeons need to correctly identify bone fragments using 2D/3D CT images before trauma surgery. Advances in deep learning technology provide good insights into trauma surgery over manual diagnosis. This study demonstrates the application of the DeepLab v3+ -based deep learning model for the automatic segmentation of fragments of the fractured tibia and fibula from CT images and the results of the evaluation of the performance of the automatic segmentation. The deep learning model, which was trained using over 11 million images, showed good performance with a global accuracy of 98.92%, a weighted intersection over the union of 0.9841, and a mean boundary F1 score of 0.8921. Moreover, deep learning performed 5-8 times faster than the experts' recognition performed manually, which is comparatively inefficient, with almost the same significance. This study will play an important role in preoperative surgical planning for trauma surgery with convenience and speed.

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

Purpose: To compare biomechanical and clinical properties of the novel internal fixation Interlocking Hip Screw (IHS) and conventional inverted triangle cannulated screws (ITCS) for treatment of Pauwels Ⅲ femoral neck fractures. Methods: Twenty synthetic femurs were osteotomized to simulate 70° Pauwels Ⅲ femoral neck fractures and randomly divided into two groups: Group IHS and Group ITCS. Specimens were loaded in quasi-static ramped and cyclical compression testing in 25° adduction to analyze for axial stiffness, failure load, and interfragmentary displacement. 21 matched patients with Pauwels Ⅲ femoral neck fracture who received closed reduction and internal fixation from January 2020 to January 2021 in both Group IHS and Group ITCS. Demographic data, time to surgery, operating duration, intraoperative blood loss, number of fluoroscopies, length of hospital stay, fracture healing time, Harris Hip Score (HHS), the score of Visual Analogue Scale (VAS) and complications such as nonunion, avascular necrosis, and femoral neck shortening were compared. Results: All specimens in the two groups survived in the axial and cyclical compression test. The axial stiffness was significantly higher for Group IHS (277.80 ± 26.58 N/mm) versus Group ITCS (205.33 ± 10.46 N/mm), p < 0.05. The maximum failure loading in Group IHS performed significantly higher than in Group ITCS (1,400.48 ± 71.60 N versus 996.76 ± 49.73 N, p < 0.05). The interfragmentary displacement of the cyclic loading test for Groups IHS and Group ITCS was 1.15 ± 0.11 mm and 1.89 ± 0.14 mm, respectively, p < 0.05. No significant difference was found in terms of demographic data, time to surgery, intraoperative blood loss, length of hospital stay and the occurrence of nonunion and avascular necrosis between groups. Shorter operating duration and fewer intraoperative fluoroscopic views were noticed using IHS compare to ITCS, p < 0.05. The HHS was 72.14 ± 5.76 and 86.62 ± 5.01 in Group IHS, and was 67.29 ± 5.27 and 81.76 ± 5.13 in Group ITCS at 3-month and 6-month follow-up, respectively, p < 0.05. The magnitude of femoral neck shortening was significantly lower in Group IHS compared to Group ITCS (4.80 ± 1.03 mm versus 5.56 ± 1.21 mm, p < 0.05). Conclusion: Our study demonstrated that IHS provided better biomechanical and clinical performance due to its unique biological and biomechanical mechanisms, compared with ITCS. Thus, IHS is a feasible alternative to ITCS for the fixation of Pauwels Ⅲ femoral neck fractures.