Influence of preoperative simulation on the reduction quality and clinical outcomes of open reduction and internal fixation for complex proximal humerus 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.

3D printing assisted MIPO for treatment of complex middle-proximal humeral shaft fractures

BACKGROUND

This study was designed to explore the clinical efficacy of 3-dimensional (3D) printing assisted minimally invasive percutaneous plate osteosynthesis (MIPO) technique by comparing the clinical outcomes with traditional open reduction and internal plating fixation (ORIF) for treating complex middle-proximal humerus fractures (AO 12C fracture type).

MATERIALS AND METHODS

The data of 42 participants who received a complicated middle-proximal humerus fracture from the beginning of 2018 to the end of 2022 were retrospectively analyzed. All patients were assigned to two groups: MIPO with detailed preoperative planning assisted by 3D printing technique (MIPO group), and traditional ORIF (ORIF group).

RESULTS

This study included 21 patients in the ORIF group and 21 patients in the MIPO group. All patients were followed-up for at least one year (mean: 16.12 ± 4.13 months), and no difference was observed in the range of shoulder joint motion (ROM), Quick Disabilities of the Arm, Shoulder and Hand (QuickDASH) scores and Constant scores between the two groups. However, the occurrence of complications (surgical incision site infection, implant loosening, bone nonunion and radial nerve palsy) in ORIF group was remarkably higher compared to the MIPO group. All the cases achieved bone union within the MIPO group. Significant differences were found in surgical time, intraoperative blood loss and fracture healing time between the two groups.

CONCLUSION

Preoperative 3D printing assisted MIPO technique exhibits obvious advantages in high operational efficiency and low occurrence of complications, which is worthy of clinical application for treating complex middle-proximal humeral shaft fractures.