Modeling stability post zygomatic fracture reconstruction.

Zygomaticomaxillary complex (ZMC) fracture repair is one of the most common surgical procedures performed in craniomaxillofacial trauma management. Miniplates and screws are used to stabilize the fractured bone using small local incisions, however, these procedures are not infrequently associated with hardware-related post-operative complications. The amount of fixation hardware utilized varies depending on the fracture pattern and surgical judgment, with three-point fixation being the conventionally accepted treatment. However, limited experimental testing and clinical studies have suggested that ZMC stabilization may be achieved with less than three-point fixation. In this study, we utilized a previously developed finite element modeling approach that allows for detailed bone and muscle representation to study the mechanical behavior of the fractured craniomaxillofacial skeleton (CMFS) under one, two, or three-point fixation of the ZMC. Results suggest that using a miniplate along the infraorbital rim in three-point fixation increases the amount of strain and load transfer to this region, rather than offloading the bone. Two-point (zygomaticomaxillary and zygomaticofrontal) fixation yielded strain patterns most similar to the intact CMFS. One-point (zygomaticofrontal) fixation resulted in higher tensile and compressive strains in the zygomaticofrontal region and the zygomatic arch, along with a higher tensile strain on the zygomatic body. These modeling results provide biomechanical evidence for the concept of over-engineering in the stabilization of facial fractures. Furthermore, they support previous suggestions that less than three-point fixation of ZMC fractures may be adequate to achieve uneventful healing.

BoneTape: A novel osteosynthetic device for the stabilization of zygomatic fractures.

BACKGROUND: The study aims to assess the safety and effectiveness of BoneTape™, a new resorbable bone fixation device, using a zygomatic fracture model in rabbits. METHODS: The study followed BoneTape™ samples and control (sham) groups over 2-, 6-, and 12-week periods post-zygomaticomaxillary (ZM) osteotomy and zygomaticofrontal (ZF) disarticulation. The osteotomized segments were analyzed for bone healing, inflammatory response, and tissue healing. µCT imaging and histological analysis were used to examine the axial alignment, offset, and quality of new bone formation. RESULTS: BoneTape™ samples demonstrated enhanced maintenance of the initial intraoperative positioning, reduced axial offset, and better alignment when compared with the control group, enabling stable bone healing under physiological loading conditions. Complete union was observed at 12-weeks in both groups. The BoneTape™ group experienced minimal immune and tissue reactions, classically associated with wound healing, and showed an increased number of giant cells at 6 and 12-weeks. CONCLUSION: BoneTape™ represents a promising advancement in osteosynthesis, demonstrating efficacy in maintaining stable zygomatic reconstruction and eliciting minimal immune response in a rabbit model. This study introduces BoneTape™ as a disruptive solution specifically designed for clinical application in cranio-maxillofacial fracture fixation, with the potential to eliminate the use of over-engineered solutions while offering benefits such as ease of application and fewer biologically disruptive steps.

Treatment affects load to failure and microdamage accumulation in healthy and osteolytic rat vertebrae.

Bone turnover and microdamage are impacted by the presence of skeletal metastases which can contribute to increased fracture risk. Treatments for metastatic disease may further impact bone quality. This exploratory study aimed to establish an initial understanding of microdamage accumulation and load to failure in healthy and osteolytic rat vertebrae following focal and systemic cancer treatment (docetaxel (DTX), stereotactic body radiotherapy (SBRT), or zoledronic acid (ZA)). Osteolytic spine metastases were developed in 6-week-old athymic female rats via intracardiac injection of HeLa human cervical cancer cells (day 0). Additional rats served as healthy controls. Rats were either untreated, received SBRT to the T10-L6 vertebrae on day 14 (15 Gy, two fractions), DTX on day 7 or 14, or ZA on day 7. Rats were euthanized on day 21. Tumor burden was assessed with bioluminescence images acquired on day 14 and 21, histology of the excised T11 and L5 vertebrae, and ex-vivo µCT images of the T13-L4. Microstructural parameters (bone volume/total volume, trabecular number, spacing, thickness, and bone mineral density) were measured from L2 vertebrae. Load to failure was measured with axial compressive loading of the L1-L3 motion segments. Microdamage accumulation was labeled in T13 vertebrae with BaSO4 staining and was visualized with high resolution µCT imaging. Microdamage volume fraction was defined as the ratio of BaSO4 to bone volume. DTX administered on day 7 reduced tumor growth significantly (p < 0.05). Microdamage accumulation was found to be increased by the presence of metastases but was reduced by all treatments with ZA showing the largest improvement in HeLa cell injected rats. Load to failure was decreased in untreated and SBRT HeLa cell injected rats compared to healthy controls (p < 0.01). There was a moderate negative correlation between load to failure and microdamage volume fraction in vertebrae from rats injected with HeLa cells (R = -0.35, p = 0.031). Strong correlations were also found between microstructural parameters and load to failure and microdamage accumulation. Several factors, including the presence of osteolytic lesions and use of cancer therapies, influence microdamage accumulation and load to failure in rat vertebrae. Understanding the impact of these treatments on fracture risk of metastatic vertebrae is important to improve management of patients with spinal metastases.

Accuracy of Orbital Shape Reconstruction-Comparative Analysis of Errors in Implant Shape Versus Implant Positioning: A Cadaveric Study.

INTRODUCTION: Orbital blowout fractures are commonly reconstructed with implants shaped to repair orbital cavity defects, restore ocular position and projection, and correct diplopia. Orbital implant shaping has traditionally been performed manually by surgeons, with more recent use of computer-assisted design (CAD). Accuracy of implant placement is also key to reconstruction. This study compares the placement accuracy of orbital implants, testing the hypothesis that CAD-shaped implants indexed to patient anatomy will better restore orbit geometry compared with manually shaped implants and manually placed implants. METHODS: The placement accuracy of orbital implants was assessed within a cadaveric blowout fracture model (3 skulls, 6 orbits) via 3-dimensional CT analysis. Defects were repaired with 4 different techniques: manually placed-manually shaped composite (titanium-reinforced porous polyethylene), manually placed CAD composite, indexed placed CAD composite, and indexed placed CAD titanium mesh. RESULTS: Implant placement accuracy differed significantly with the implant preparation method ( P =0.01). Indexing significantly improved the placement accuracy ( P =0.002). Indexed placed titanium mesh CAD implants (1.42±0.33 mm) were positioned significantly closer to the intact surface versus manually placed-manually shaped composite implants (2.12±0.39 mm). DISCUSSION: Computer-assisted design implants indexed to patient geometry yielded average errors below the acceptable threshold (2 mm) for enophthalmos and diplopia. This study highlights the importance of adequately indexing CAD-designed implants to patient geometry to ensure accurate orbital reconstructions.

Artificial Intelligence-Based Modeling Can Predict Face Shape Based on Underlying Craniomaxillofacial Bone.

Reconstructing facial deformities is often challenging due to the complex 3-dimensional (3D) anatomy of the craniomaxillofacial skeleton and overlying soft tissue structures. Bilateral injuries cannot benefit from mirroring techniques and as such preinjury information (eg, 2D pictures or 3D imaging) may be utilized to determine or estimate the desired 3D face shape. When patient-specific information is not available, other options such as statistical shape models may be employed; however, these models require registration to a consistent orientation which may be challenging. Artificial intelligence (AI) has been used to identify facial features and generate highly realistic simulated faces. As such, it was hypothesized that AI can be used to predict 3D face shape by learning its relationship with the underlying bone surface anatomy in a subject-specific manner. An automated image processing and AI modeling workflow using a modified 3D UNet was generated to estimate 3D face shape using the underlying bone geometry and additional metadata (eg, body mass index and age) obtained from 5 publicly available computed tomography imaging datasets. Visually, the trained models provided a reasonable prediction of the contour and geometry of the facial tissues. The pipeline achieved a validation dice=0.89 when trained on the combined 5 datasets, with the highest dice=0.925 achieved with the single HNSCC dataset. Estimated predefect facial geometry may ultimately be used to aid preoperative craniomaxillofacial surgical planning, providing geometries for intraoperative templates, guides, navigation, molds, and forming tools. Automated face shape prediction may additionally be useful in forensic studies to aid in the identification of unknown skull remains.

Probiotics: Can it modulate fracture healing?

OBJECTIVE: Fractures remain a huge burden and their management adversely affects individuals' function and productivity during the lengthy healing period. Gut microbiota exerts a systemic influence on diverse aspects of host physiology, including bone. The primary objective of this study was to evaluate if oral probiotic treatment before or after a fracture in a mouse model could increase cytokines and biomarkers essential for bone healing with subsequent improvement in the biomechanical properties of the healed callus. METHODS: Femoral osteotomy and intramedullary pinning were performed on C57BL/6 mice. Group 1 received either control PBS or probiotic via oral gavage for 5 weeks before fracture (pre-fracture). Group 2 received equivalent treatments for 4 weeks only after fracture (post-fracture). Fracture calluses were harvested on day 3 and 7 for RT-qPCR to quantify osteogenic-related inflammatory cytokines and bone biomarkers. Fractured femurs were evaluated day 28 post-osteotomy via microstructural analysis (µCT) and biomechanical testing (torsion). RESULTS: Mice treated with probiotics pre-fracture (group 1) showed significantly increased gene expression on day 3 of cytokines TGF-ß, IL-6 and IL-17F and a corresponding increase in gene expression on day 7 for Col1 and Runx2. Significant improvement was also seen in bone volume fraction, bone mineral density, tissue mineral density, maximum yield torque, stiffness and strain energy. Mice treated with probiotics post-fracture (group 2), demonstrated no changes in cytokine or bone marker gene expression with no significant changes on microstructural analysis. However, significant increases were seen in twist angle at failure and strain energy, with a corresponding reduction in torsional stiffness. CONCLUSION: Our results suggest that oral probiotic administration, before or after a fracture, may sufficiently alter the gut flora microenvironment leading to improved bone healing biomechanical properties. The use of probiotics may provide a cost-effective and low-risk adjunctive therapy to improve fracture healing.

Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge.

Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects. These implants are usually generated offline and may require days to weeks to be available. An automated implant design process combined with onsite manufacturing facilities can guarantee immediate implant availability and avoid secondary intervention. To address this need, the AutoImplant II challenge was organized in conjunction with MICCAI 2021, catering for the unmet clinical and computational requirements of automatic cranial implant design. The first edition of AutoImplant (AutoImplant I, 2020) demonstrated the general capabilities and effectiveness of data-driven approaches, including deep learning, for a skull shape completion task on synthetic defects. The second AutoImplant challenge (i.e., AutoImplant II, 2021) built upon the first by adding real clinical craniectomy cases as well as additional synthetic imaging data. The AutoImplant II challenge consisted of three tracks. Tracks 1 and 3 used skull images with synthetic defects to evaluate the ability of submitted approaches to generate implants that recreate the original skull shape. Track 3 consisted of the data from the first challenge (i.e., 100 cases for training, and 110 for evaluation), and Track 1 provided 570 training and 100 validation cases aimed at evaluating skull shape completion algorithms at diverse defect patterns. Track 2 also made progress over the first challenge by providing 11 clinically defective skulls and evaluating the submitted implant designs on these clinical cases. The submitted designs were evaluated quantitatively against imaging data from post-craniectomy as well as by an experienced neurosurgeon. Submissions to these challenge tasks made substantial progress in addressing issues such as generalizability, computational efficiency, data augmentation, and implant refinement. This paper serves as a comprehensive summary and comparison of the submissions to the AutoImplant II challenge. Codes and models are available at https://github.com/Jianningli/Autoimplant_II.

Is three-point fixation needed to mechanically stabilize zygomaticomaxillary complex fractures?

Fixation is critical in zygomaticomaxillary complex (ZMC) fractures to avoid malunion; however, controversy exists as to how much hardware is required to achieve adequate stability. Current fixation regimens may not represent the minimum stabilization needed for uneventful healing. Craniomaxillofacial (CMF) computational models have shown limited load transmission through the infraorbital rim (IOR), and a previous experimental study of ZMC fractures has suggested that IOR plating does not alter CMF bone strain patterns. This study aimed to measure the impact of stabilization on fracture site displacement under muscle loading, testing the hypothesis that three-point fixation is not critical for ZMC fracture stability. Four ZMC complex fractures were simulated on two cadaveric samples and stabilized with three-point plating. Displacements simulating mouth openings of 20 mm and 30 mm were applied to the mandible using a custom apparatus. Fracture gap displacement under load was measured at multiple points along each fracture line, and bone strain was captured using a combination of uniaxial and rosette gauges. Data capture was repeated with the IOR plate removed (two-point fixation) and with the zygomaticomaxillary plate removed (one-point fixation). Fracture displacement under muscle loading was consistent, with gaps of less than 1 mm in 95% of cases (range 0.05-1.44 mm), reflecting clinical stability. Large variabilities were observed in the strain measurements, which may reflect the complexity of CMFS load patterns and the sensitivity of strain values to gauge placement. This study supports the concept of hardware reduction, suggesting that two-point (or even one-point) fixation may provide sufficient stability for a ZMC fracture under applied muscle loading.

Development and validation of a radiofrequency ablation treatment planning system for vertebral metastases.

PURPOSE: Bone-targeted radiofrequency ablation (RFA) is widely used in the treatment of vertebral metastases. While radiation therapy utilizes established treatment planning systems (TPS) based on multimodal imaging to optimize treatment volumes, current RFA of vertebral metastases has been limited to qualitative image-based assessment of tumour location to direct probe selection and access. This study aimed to design, develop and evaluate a computational patient-specific RFA TPS for vertebral metastases. METHODS: A TPS was developed on the open-source 3D slicer platform, including procedural setup, dose calculation (based on finite element modelling), and analysis/visualization modules. Usability testing was carried out by 7 clinicians involved in the treatment of vertebral metastases on retrospective clinical imaging data using a simplified dose calculation engine. In vivo evaluation was performed in a preclinical porcine model (n = 6 vertebrae). RESULTS: Dose analysis was successfully performed, with generation and display of thermal dose volumes, thermal damage, dose volume histograms and isodose contours. Usability testing showed an overall positive response to the TPS as beneficial to safe and effective RFA. The in vivo porcine study showed good agreement between the manually segmented thermally damaged volumes vs. the damage volumes identified from the TPS (Dice Similarity Coefficient = 0.71 ± 0.03, Hausdorff distance = 1.2 ± 0.1 mm). CONCLUSION: A TPS specifically dedicated to RFA in the bony spine could help account for tissue heterogeneities in both thermal and electrical properties. A TPS would enable visualization of damage volumes in 2D and 3D, assisting clinicians in decisions about potential safety and effectiveness prior to performing RFA in the metastatic spine.

Key Stakeholder Perceptions of Standard vs. Total Cost of Ownership: A Procurement Analysis for Orthopaedic-Powered Instruments.

This study compares standard procurement methodology (SPM) with total cost of ownership (TCO) methodology for the procurement of orthopaedic-powered instruments. The authors conducted semi-structured standardized interviews with key hospital procurement stakeholders following consolidated criteria for reporting qualitative research. Of the 33 hospital procurement stakeholders interviewed, all (100%) reported that SPM would be easier to use than TCO. However, only six (18%) preferred SPM over TCO. Barriers to the adoption of TCO emerged as a theme. Creating TCO frameworks can help to simplify the process for procurement agents and facilitate its adoption in the healthcare sector.

Predicting Pathological Fractures at Metastatic Humeral Lesions.

The humerus is the second most common site of metastatic disease involving long bones, yet it is still unclear which patients are at high risk for a fracture and may require prophylactic surgical fixation. The goal of this study was to assess the validity of the Mirels score to predict fractures of metastatic lesions in the humerus. Methods: We performed a retrospective electronic chart review of patients with humeral metastases at our institution (2005 to 2021), with 188 patients meeting the inclusion criteria. Sixty-one of the patients developed a fracture during follow-up. The metastatic humeral lesions were scored according to the Mirels rating system and additional radiographic criteria (cortical breach, location within the humerus, number of lesions). The predictive value of each Mirels score cutoff for fracture was assessed using sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and multivariate logistic regression. Survivorship until fracture was analyzed for each Mirels score cutoff using Kaplan-Meier curves and the log-rank test. Significance was set at p < 0.01. Results: There were no significant differences in age, sex, side of the lesion, type of malignancy, and radiation dose between the groups with and without fracture (all p > 0.01). A Mirels score of ≥8 points had the best predictive profile, with sensitivity of 83.6%, specificity of 79.5%, and AUC of 0.82 (95% confidence interval [CI], 0.75 to 0.88, p < 0.01). A logistic regression model also demonstrated that a Mirels score of ≥8 (odds ratio = 5.8, 95% CI = 1.9 to 18.2, p < 0.01) and a cortical breach (odds ratio = 21.0, 95% CI = 5.7 to 77.2, p < 0.01) were significant predictors of pathological fracture. No other radiographic characteristics were found to be significant predictors of fracture. Conclusions: This study indicated that a Mirels score of ≥8 points had the best predictive profile for anticipating fractures at a metastasis in the humerus. This is in contrast to the traditional Mirels definition of an impending pathological fracture that is used for the lower extremity, a score of ≥9. Additionally, the presence of a cortical breach was a significant predictor of fracture risk. Level of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Evaluation of at-home physiotherapy.

An objective technological solution for tracking adherence to at-home shoulder physiotherapy is important for improving patient engagement and rehabilitation outcomes, but remains a significant challenge. The aim of this research was to evaluate performance of machine-learning (ML) methodologies for detecting and classifying inertial data collected during in-clinic and at-home shoulder physiotherapy exercise. A smartwatch was used to collect inertial data from 42 patients performing shoulder physiotherapy exercises for rotator cuff injuries in both in-clinic and at-home settings. A two-stage ML approach was used to detect out-of-distribution (OOD) data (to remove non-exercise data) and subsequently for classification of exercises. We evaluated the performance impact of grouping exercises by motion type, inclusion of non-exercise data for algorithm training, and a patient-specific approach to exercise classification. Algorithm performance was evaluated using both in-clinic and at-home data. The patient-specific approach with engineered features achieved the highest in-clinic performance for differentiating physiotherapy exercise from non-exercise activity (area under the receiver operating characteristic (AUROC) = 0.924). Including non-exercise data in algorithm training further improved classifier performance (random forest, AUROC = 0.985). The highest accuracy achieved for classifying individual in-clinic exercises was 0.903, using a patient-specific method with deep neural network model extracted features. Grouping exercises by motion type improved exercise classification. For at-home data, OOD detection yielded similar performance with the non-exercise data in the algorithm training (fully convolutional network AUROC = 0.919). Including non-exercise data in algorithm training improves detection of exercises. A patient-specific approach leveraging data from earlier patient-supervised sessions should be considered but is highly dependent on per-patient data quality.

Improving Resource Utilization for Arthroplasty Care by Leveraging Machine Learning and Optimization: A Systematic Review.

Background: There is a growing demand for total joint arthroplasty (TJA) surgery. The applications of machine learning (ML), mathematical optimization, and computer simulation have the potential to improve efficiency of TJA care delivery through outcome prediction and surgical scheduling optimization, easing the burden on health-care systems. The purpose of this study was to evaluate strategies using advances in analytics and computational modeling that may improve planning and the overall efficiency of TJA care. Methods: A systematic review including MEDLINE, Embase, and IEEE Xplore databases was completed from inception to October 3, 2022, for identification of studies generating ML models for TJA length of stay, duration of surgery, and hospital readmission prediction. A scoping review of optimization strategies in elective surgical scheduling was also conducted. Results: Twenty studies were included for evaluating ML predictions and 17 in the scoping review of scheduling optimization. Among studies generating linear or logistic control models alongside ML models, only 1 found a control model to outperform its ML counterpart. Furthermore, neural networks performed superior to or at the same level as conventional ML models in all but 1 study. Implementation of mathematical and simulation strategies improved the optimization efficiency when compared to traditional scheduling methods at the operational level. Conclusions: High-performing predictive ML-based models have been developed for TJA, as have mathematical strategies for elective surgical scheduling optimization. By leveraging artificial intelligence for outcome prediction and surgical optimization, there exist greater opportunities for improved resource utilization and cost-savings in TJA than when using traditional modeling and scheduling methods.

Artificial Intelligence for Hip Fracture Detection and Outcome Prediction: A Systematic Review and Meta-analysis.

Importance: Artificial intelligence (AI) enables powerful models for establishment of clinical diagnostic and prognostic tools for hip fractures; however the performance and potential impact of these newly developed algorithms are currently unknown. Objective: To evaluate the performance of AI algorithms designed to diagnose hip fractures on radiographs and predict postoperative clinical outcomes following hip fracture surgery relative to current practices. Data Sources: A systematic review of the literature was performed using the MEDLINE, Embase, and Cochrane Library databases for all articles published from database inception to January 23, 2023. A manual reference search of included articles was also undertaken to identify any additional relevant articles. Study Selection: Studies developing machine learning (ML) models for the diagnosis of hip fractures from hip or pelvic radiographs or to predict any postoperative patient outcome following hip fracture surgery were included. Data Extraction and Synthesis: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses and was registered with PROSPERO. Eligible full-text articles were evaluated and relevant data extracted independently using a template data extraction form. For studies that predicted postoperative outcomes, the performance of traditional predictive statistical models, either multivariable logistic or linear regression, was recorded and compared with the performance of the best ML model on the same out-of-sample data set. Main Outcomes and Measures: Diagnostic accuracy of AI models was compared with the diagnostic accuracy of expert clinicians using odds ratios (ORs) with 95% CIs. Areas under the curve for postoperative outcome prediction between traditional statistical models (multivariable linear or logistic regression) and ML models were compared. Results: Of 39 studies that met all criteria and were included in this analysis, 18 (46.2%) used AI models to diagnose hip fractures on plain radiographs and 21 (53.8%) used AI models to predict patient outcomes following hip fracture surgery. A total of 39 598 plain radiographs and 714 939 hip fractures were used for training, validating, and testing ML models specific to diagnosis and postoperative outcome prediction, respectively. Mortality and length of hospital stay were the most predicted outcomes. On pooled data analysis, compared with clinicians, the OR for diagnostic error of ML models was 0.79 (95% CI, 0.48-1.31; P = .36; I2 = 60%) for hip fracture radiographs. For the ML models, the mean (SD) sensitivity was 89.3% (8.5%), specificity was 87.5% (9.9%), and F1 score was 0.90 (0.06). The mean area under the curve for mortality prediction was 0.84 with ML models compared with 0.79 for alternative controls (P = .09). Conclusions and Relevance: The findings of this systematic review and meta-analysis suggest that the potential applications of AI to aid with diagnosis from hip radiographs are promising. The performance of AI in diagnosing hip fractures was comparable with that of expert radiologists and surgeons. However, current implementations of AI for outcome prediction do not seem to provide substantial benefit over traditional multivariable predictive statistics.

Sarcopenia in Men With Bone-Predominant Metastatic Castration-Resistant Prostate Cancer Undergoing Ra-223 Therapy.

INTRODUCTION: Osteosarcopenia is the progressive loss of musculoskeletal structure and functionality, contributing to disability and mortality. Despite complex interactions between bone and muscle, osteosarcopenia prevention and treatment in men with metastatic castration-resistant prostate cancer (mCRPC) focuses predominantly on bone health. It is unknown whether Radium-223 (Ra-223) therapy affects sarcopenia. METHODS: We identified 52 patients with mCRPC who had received Ra-223 and had a baseline plus ≥1 follow-up abdominopelvic CT scan. The total contour area (TCA) and averaged Hounsfield units (HU) of the left and right psoas muscles were obtained at the inferior L3 endplate, and the psoas muscle index (PMI) was calculated therefrom. Intrapatient musculoskeletal changes were analyzed across various time points. RESULTS: TCA and PMI gradually declined over the study period (P = .002, P = .003, respectively), but Ra-223 therapy did not accelerate sarcopenia, nor the decline of HU compared to the pre-Ra-223 period. The median overall survival of patients with baseline sarcopenia was numerically worse (14.93 vs. 23.23 months, HR 0.612, P = .198). CONCLUSIONS: Ra-223 does not accelerate sarcopenia. Thus, worsening muscle parameters in men with mCRPC undergoing Ra-223 therapy are attributable to other factors. Further research is needed to determine whether baseline sarcopenia predicts poor overall survival in such patients.

Open-source pipeline for automatic segmentation and microstructural analysis of murine knee subchondral bone.

µCT images are commonly analysed to assess changes in bone density and microstructure in preclinical murine models. Several platforms provide automated analysis of bone microstructural parameters from volumetric regions of interest (ROI). However, segmentation of the regions of subchondral bone to create the volumetric ROIs remains a manual and time-consuming task. This study aimed to develop an automated end-to-end pipeline, combining segmentation and microstructural analysis, to evaluate subchondral bone in the mouse proximal knee. METHODS: A segmented dataset of µCT scans from 62 knees (healthy and arthritic) from 10-week male C57BL/6 mice was used to train a U-Net type architecture to automate segmentation of the subchondral trabecular bone. These segmentations were used in tandem with the original scans as input for microstructural analysis along with thresholded trabecular bone. Manually and U-Net segmented ROIs were fed into two available pipelines for microstructural analysis: the ITKBoneMorphometry library and CTan (SKYSCAN). Outcome parameters were compared between pipelines, including: bone volume (BV), total volume (TV), BV/TV, trabecular number (TbN), trabecular thickness (TbTh), trabecular separation (TbSp), and bone surface density (BSBV). RESULTS: There was good agreement for all bone measures comparing the manual and U-Net pipelines utilizing ITK (R = 0.88-0.98) and CTAn (R = 0.91-0.98). ITK and CTAn showed good agreement for BV, TV, BV/TV, TbTh and BSBV (R = 0.9-0.98). However, limited agreement was seen between TbN (R = 0.73) and TbSb (R = 0.59) due to methodological differences in how spacing is evaluated. Microstructural parameters generated from manual and automatic segmentations showed high correlation across all measures. Using the CTAn pipeline yielded strong R2 values (0.83-0.96) and very strong agreement based on ICC (0.90-0.98). The ITK pipeline yielded similarly high R2 values (0.91-0.96, except for TbN (0.77)), and ICC values (0.88-0.98). The automated segmentations yield lower average values for BV, TV and BV/TV (ranging from 14 % to 6.3 %), but differences were not found to be influenced by the mean ROI values. CONCLUSIONS: This integrated pipeline seamlessly automated both segmentation and quantification of the proximal tibia subchondral bone microstructure. This automated pipeline allows the analysis of large volumes of data, and its open-source nature may enable the standardization of microstructural analysis of trabecular bone across different research groups.

Biomechanical Comparison of 3 Medial Patellofemoral Complex Reconstruction Techniques Shows Medial Overconstraint but No Significant Difference in Patella Lateralization and Contact Pressure.

PURPOSE: The purpose of this study was to investigate biomechanical differences of medial patellofemoral ligament (MPFL) reconstruction, medial quadriceps tendon femoral ligament (MQTFL) reconstruction, and a combination of these techniques to restore lateral patellar constraint and contact pressures. METHODS: Eight fresh frozen cadaver knees were mounted to a custom jig with physiological quadriceps tendon loading. Flexion angles and contact pressure (CP) were dynamically measured using Tekscan® pressure sensors and Polhemus® Liberty 6 degree of freedom (6DOF) positioning sensors in the following conditions: 1) intact 2) MPFL and MQTFL deficient, 3) MPFL reconstructed, 4) Combined MPFL + MQTFL reconstructed, and 5) MQTFL reconstructed. Lateral patellar translation was tested using horizontally directed 30 N force applied at 30° of knee flexion. The knees were flexed in dynamic fashion, and CP values were recorded for 10°, 20°, 30°, 50°, 70°, and 90° degrees of flexion. Group differences were assessed with ANOVA's followed by pairwise comparisons with Bonferroni correction. RESULTS: MPFL (P = .002) and combined MPFL/MQTFL (P = .034) reconstruction significantly reduced patellar lateralization from +19.28% (9.78%, 28.78%) in the deficient condition to -17.57% (-27.84%, -7.29%) and -15.56% (-33.61%, 2.30%), respectively. MPFL reconstruction was most restrictive and MQTFL reconstruction the least -7.29% (-22.01%, 7.45%). No significant differences were found between the three reconstruction techniques. Differences in CP between the three reconstruction techniques were not significant (<.02 MPa) at all flexion angles. CONCLUSION: The present study found no significant difference for patellar lateralization and patellofemoral CP between MPFL, combined MPFL/MQTFL, and MQTFL reconstruction. All 3 techniques resulted in stronger lateral patellar constraint compared to the native state, while the MQTFL reconstruction emulated the intact state the closest. CLINICAL RELEVANCE: Various surgical techniques for medial patellofemoral complex reconstruction can restore patellar stability with similar patellofemoral articular pressures.

Assessing the Nasal Midline in Rhinoplasty: How Good Are We?

BACKGROUND: The challenge of assessing nasal alignment and asymmetry can contribute to high revision rates in rhinoplasty. Comparing to a validated computer algorithm for nasal alignment, the accuracy with which plastic surgeons can assess deviation of the nasal midline from the facial midline was measured. METHODS: Using 20 faces from the Binghamton University 3-dimensional face database, deviation was evaluated from facial midline of the middorsal line for the upper, middle, and lower thirds of the nose. Surgeons were asked to assess extent of deviation from facial midline for each third of the nose using a linear analog scale. Spearman correlations were performed comparing the surgeons' results to the algorithm measurements. Eleven residents and 9 consultant surgeons were tested. RESULTS: Surgeons' assessment of deviation correlated poorly with the algorithm in the upper third ( r =0.32, P <0.0001) and moderately in the middle third ( r =0.49, P <0.0001) and lower third ( r =0.41, P <0.0001) of the nose. No difference in accuracy was found between trainee and consultant surgeons ( P =0.51), and greater experience (>10 y performing nasal surgery) did not significantly affect performance ( P =0.15). The effect of fatigue on the accuracy of assessment was found to be significant ( P =0.0009). CONCLUSIONS: Surgeons have difficulty in visually assessing the 3-dimensional nasal midline irrespective of experience, and surgeon fatigue was found to be adversely affect the accuracy of assessments.

Multi-Modal Deep Learning for Assessing Surgeon Technical Skill.

This paper introduces a new dataset of a surgical knot-tying task, and a multi-modal deep learning model that achieves comparable performance to expert human raters on this skill assessment task. Seventy-two surgical trainees and faculty were recruited for the knot-tying task, and were recorded using video, kinematic, and image data. Three expert human raters conducted the skills assessment using the Objective Structured Assessment of Technical Skill (OSATS) Global Rating Scale (GRS). We also designed and developed three deep learning models: a ResNet-based image model, a ResNet-LSTM kinematic model, and a multi-modal model leveraging the image and time-series kinematic data. All three models demonstrate performance comparable to the expert human raters on most GRS domains. The multi-modal model demonstrates the best overall performance, as measured using the mean squared error (MSE) and intraclass correlation coefficient (ICC). This work is significant since it demonstrates that multi-modal deep learning has the potential to replicate human raters on a challenging human-performed knot-tying task. The study demonstrates an algorithm with state-of-the-art performance in surgical skill assessment. As objective assessment of technical skill continues to be a growing, but resource-heavy, element of surgical education, this study is an important step towards automated surgical skill assessment, ultimately leading to reduced burden on training faculty and institutes.

Wear reduction of orthopaedic implants through Cryogenic Thermal Cycling.

BACKGROUND: Material wear caused by relative micromotion of multi-part orthopaedic implants can impact physiology near the implant site and ultimately lead to revision surgery. Cryogenic Thermal Cycling (CTC) is a process that refines and stabilizes the crystal lattice structure of materials by introducing them to temperature modulation cycles that include extremely low temperatures. This method has been proven to significantly improve the performance and wear life capabilities of mechanical components in an efficient, sterile and non-polluting process. This technical note is aimed to evaluate the impact of CTC on wear resistance at head-neck taper connections used in total hip arthroplasty (THA). METHODS: Mock up components (heads and trunnions) using the same materials representative of THA implants were manufactured for this study. Components were placed into a custom-designed cooling chamber in which they underwent CTC. Short-term Incremental Cyclic Fretting Corrosion (ICFC) testing was conducted on thermally cycled and control (untreated) components, subjected to a full simulated gait with periodically increasing incremental peak loads (modified BioPuls Dual Station ASTM Hip Simulator). Corrosion-related electrical activity was measured for samples with and without CTC. A long-term wear test was also conducted to evaluate corrosion and fretting under double peak compression loading (no rotation). RESULTS: From the short-term tests, samples that underwent CTC showed a significantly reduced corrosion current at the maximum testing compression load of 3,300 N (p = 0.048). The elevated corrosion onset load seen in the treated components did not reach significance (p = 0.152) and no significant difference in the mean change in open circuit potential (OCP) was observed (p = 0.471). Longer-term wear testing found variable differences in CTC and control components with respect to damaged surface areas. CONCLUSION: CTC was shown to be a viable method to reduce short-term corrosion-related electrical activity in THA head-neck taper connections. While consistent differences were not seen in longer-term measures of corrosion, further study is warranted to evaluate the potential of CTC in the context of reducing wear in modular THA implants.