Investigating gender and ethnicity differences in proximal humeral morphology using a statistical shape model

Accuracy tradeoffs between individual bone and joint-level statistical shape models of knee morphology

Statistical shape models (SSMs) are useful tools in evaluating variation in bony anatomy to assess pathology, plan surgical interventions, and inform the design of orthopaedic implants and instrumentation. Recently, by considering multiple bones spanning a joint or the whole lower extremity, SSMs can support studies investigating articular conformity and joint mechanics. The objective of this study was to assess tradeoffs in accuracy between SSMs of the femur or tibia individually versus a combined joint-level model. Three statistical shape models were developed (femur-only, tibia-only, and joint-level) for a training set of 179 total knee arthroplasty (TKA) patients with osteoarthritis representing both genders and several ethnicities. Bone geometries were segmented from preoperative CT scans, meshed with triangular elements, and registered to a template for each SSM. Principal component analysis was performed to determine modes of variation. The statistical shape models were compared using measures of compactness, accuracy, generalization, and specificity. The generalization evaluation, assessing the ability to describe an unseen instance in a leave-one-out analysis, showed that errors were consistently smaller for the individual femur and tibia SSMs than for the joint-level model. However, when additional modes were included in the joint-level model, the errors were comparable to the individual bone results, with minimal additional computational expense. When developing more complex SSMs at the joint, lower limb, or whole-body level, the use of an error threshold to inform the number of included modes, instead of 95 % of the variation explained, can help to ensure accurate representations of anatomy.

Scan-Free and Fully Automatic Tracking of Native Knee Anatomy from Dynamic Stereo-Radiography with Statistical Shape and Intensity Models

Kinematic tracking of native anatomy from stereo-radiography provides a quantitative basis for evaluating human movement. Conventional tracking procedures require significant manual effort and call for acquisition and annotation of subject-specific volumetric medical images. The current work introduces a framework for fully automatic tracking of native knee anatomy from dynamic stereo-radiography which forgoes reliance on volumetric scans. The method consists of three computational steps. First, captured radiographs are annotated with segmentation maps and anatomic landmarks using a convolutional neural network. Next, a non-convex polynomial optimization problem formulated from annotated landmarks is solved to acquire preliminary anatomy and pose estimates. Finally, a global optimization routine is performed for concurrent refinement of anatomy and pose. An objective function is maximized which quantifies similarities between masked radiographs and digitally reconstructed radiographs produced from statistical shape and intensity models. The proposed framework was evaluated against manually tracked trials comprising dynamic activities, and additional frames capturing a static knee phantom. Experiments revealed anatomic surface errors routinely below 1.0 mm in both evaluation cohorts. Median absolute errors of individual bone pose estimates were below 1.0∘ or mm for 15 out of 18 degrees of freedom in both evaluation cohorts. Results indicate that accurate pose estimation of native anatomy from stereo-radiography may be performed with significantly reduced manual effort, and without reliance on volumetric scans.

Fully automatic tracking of native knee kinematics from stereo-radiography with digitally reconstructed radiographs

Precise measurement of joint-level motion from stereo-radiography facilitates understanding of human movement. Conventional procedures for kinematic tracking require significant manual effort and are time intensive. The current work introduces a method for fully automatic tracking of native knee kinematics from stereo-radiography sequences. The framework consists of three computational steps. First, biplanar radiograph frames are annotated with segmentation maps and key points using a convolutional neural network. Next, initial bone pose estimates are acquired by solving a polynomial optimization problem constructed from annotated key points and anatomic landmarks from digitized models. A semidefinite relaxation is formulated to realize the global minimum of the non-convex problem. Pose estimates are then refined by registering computed tomography-based digitally reconstructed radiographs to masked radiographs. A novel rendering method is also introduced which enables generating digitally reconstructed radiographs from computed tomography scans with inconsistent slice widths. The automatic tracking framework was evaluated with stereo-radiography trials manually tracked with model-image registration, and with frames which capture a synthetic leg phantom. The tracking method produced pose estimates which were consistently similar to manually tracked values; and demonstrated pose errors below 1.0 degree or millimeter for all femur and tibia degrees of freedom in phantom trials. Results indicate the described framework may benefit orthopaedics and biomechanics applications through acceleration of kinematic tracking.

Why is female gender associated with poorer clinical outcomes after reverse total shoulder arthroplasty?

INTRODUCTION

There is a lack of gender-specific research after reverse total shoulder arthroplasty (RTSA). Although previous studies have documented worse outcomes in women, a more thorough understanding of why outcomes may differ is needed. We therefore asked: (1) Are there gender-specific differences in preoperative and postoperative clinical scores, complications, surgery-related parameters, and demographics? (2) Is female gender an independent risk factor for poorer clinical outcomes after RTSA? (3) If so, why is female gender associated with poorer outcomes after RTSA?

MATERIALS AND METHODS

Between 2005 and 2019, 987 primary RTSAs were performed in our institution. After exclusion criteria were applied, data of 422 female and 271 male patients were analyzed. Clinical outcomes (absolute/relative Constant Score [a/rCS] and Subjective Shoulder Value [SSV]), complications (intra- and/or postoperative fracture, loosening), surgery-related parameters (indication, implant-related characteristics), and demographics (age, gender, body mass index, and number of previous surgeries) were evaluated. Preoperative and postoperative radiographs were analyzed (critical shoulder angle, deltoid-tuberosity index, reverse shoulder angle, lateralization shoulder angle, and distalization shoulder angle).

RESULTS

Preoperative clinical scores (aCS, rCS, SSV, and pain level) and postoperative clinical outcomes (aCS and rCS) were significantly worse in women. However, the improvement between preoperative and postoperative outcomes was significantly higher in female patients for rCS (P = .037), internal rotation (P < .001), and regarding pain (P < .001). Female patients had a significantly higher number of intraoperative and postoperative fractures (24.9% vs. 11.4%, P < .001). The proportion of female patients with a deltoid-tuberosity index <1.4 was significantly higher than males (P = .01). Female gender was an independent negative predictor for postoperative rCS (P = .047, coefficient -0.084) and pain (P = .017, coefficient -0.574). In addition to female sex per se being a predictive factor of worse outcomes, females were significantly more likely to meet 2 of the 3 most significant predictive factors: (1) significantly worse preoperative clinical scores and (2) higher rate of intra- and/or postoperative fractures.

CONCLUSIONS

Female sex is a very weak, but isolated, negative predictive factor that negatively affects the objective clinical outcome (rCS) after RTSA. However, differences did not reach the minimal clinically important difference, and it is not a predictor for the subjective outcome (SSV). The main reason for the worse outcome in female patients seems to be a combination of higher preoperative disability and higher incidence of fractures. To improve the outcome of women, all measures that contribute to the reduction of perioperative fracture risk should be used.

Prediction of the Proximal Humerus Morphology Based on a Statistical Shape Model with Two Parameters: Comparison to Contralateral Registration Method

(1) Background: Complex proximal humerus fractures often result in complications following surgical treatment. A better understanding of the full 3D displacement would provide insight into the fracture morphology. Repositioning of fracture elements is often conducted by using the contralateral side as a reconstruction template. However, this requires healthy contralateral anatomy. The purpose of this study was to create a Statistical Shape Model (SSM) and compare its effectiveness to the contralateral registration method for the prediction of the humeral proximal segment; (2) Methods: An SSM was created from 137 healthy humeri. A prediction for the proximal segment of the left humeri from eight healthy patients was made by combining the SSM with parameters. The predicted proximal segment was compared to the left proximal segment of the patients. Their left humerus was also compared to the contralateral (right) humerus; (3) Results: Eight modes explained 95% of the variation. Most deviations of the SSM prediction and the contralateral registration method were below the clinically relevant 2 mm distance threshold.; (4) Conclusions: An SSM combined with parameters is a suitable method to predict the proximal humeral segment when the contralateral CT scan is unavailable or the contralateral humerus is unhealthy, provided that the fracture pattern allows measurements of these parameters.

Metallosis-Induced Conversion Shoulder Arthroplasty: A Unique Experience and Literature Review

BACKGROUND

Total shoulder arthroplasty (TSA) can fail for several reasons, such as component loosening, periprosthetic fracture, instability, infection, soft tissue failure, or joint overstuffing. Severe metallosis without loose glenoid components after TSA may result in the need for revision to reverse TSA.

CASE PRESENTATION

Four years before the current presentation, an 86-year-old woman suffered from right shoulder pain and swelling. The initial diagnosis was osteoarthritis of the shoulder joint, for which she underwent TSA. Four years later, she complained of shoulder joint pain, swelling, and limited range of motion. On sonography, subscapularis and supraspinatus tendon tears were identified. Plain radiographs and computed tomography (CT) scans showed metallosis around the shoulder joint. Due to the rocking horse mechanism, wear of the upper portion of the glenoid component and bearing caused a foreign-body reaction and severe metallosis around the joint. Due to a massive rotator cuff tear combined with glenoid component wear, the patient eventually underwent reverse TSA (RTSA) and was satisfied with the final results.

CONCLUSIONS

Severe metallosis due to glenoid component wear combined with a massive rotator cuff tear in TSA may cause the need for revision to RTSA.

Geometric variation of the human tibia-fibula: a public dataset of tibia-fibula surface meshes and statistical shape model

Background

Variation in tibia geometry is a risk factor for tibial stress fractures. Geometric variability in bones is often quantified using statistical shape modelling. Statistical shape models (SSM) offer a method to assess three-dimensional variation of structures and identify the source of variation. Although SSM have been used widely to assess long bones, there is limited open-source datasets of this kind. Overall, the creation of SSM can be an expensive process, that requires advanced skills. A publicly available tibia shape model would be beneficial as it enables researchers to improve skills. Further, it could benefit health, sport and medicine with the potential to assess geometries suitable for medical equipment, and aid in clinical diagnosis. This study aimed to: (i) quantify tibial geometry using a SSM; and (ii) provide the SSM and associated code as an open-source dataset.

Methods

Lower limb computed tomography (CT) scans from the right tibia-fibula of 30 cadavers (male n = 20, female n = 10) were obtained from the New Mexico Decedent Image Database. Tibias were segmented and reconstructed into both cortical and trabecular sections. Fibulas were segmented as a singular surface. The segmented bones were used to develop three SSM of the: (i) tibia; (ii) tibia-fibula; and (iii) cortical-trabecular. Principal component analysis was applied to obtain the three SSM, with the principal components that explained 95% of geometric variation retained.

Results

Overall size was the main source of variation in all three models accounting for 90.31%, 84.24% and 85.06%. Other sources of geometric variation in the tibia surface models included overall and midshaft thickness; prominence and size of the condyle plateau, tibial tuberosity, and anterior crest; and axial torsion of the tibial shaft. Further variations in the tibia-fibula model included midshaft thickness of the fibula; fibula head position relative to the tibia; tibia and fibula anterior-posterior curvature; fibula posterior curvature; tibia plateau rotation; and interosseous width. The main sources of variation in the cortical-trabecular model other than general size included variation in the medulla cavity diameter; cortical thickness; anterior-posterior shaft curvature; and the volume of trabecular bone in the proximal and distal ends of the bone.

Conclusion

Variations that could increase the risk of tibial stress injury were observed, these included general tibial thickness, midshaft thickness, tibial length and medulla cavity diameter (indicative of cortical thickness). Further research is needed to better understand the effect of these tibial-fibula shape characteristics on tibial stress and injury risk. This SSM, the associated code, and three use examples for the SSM have been provided in an open-source dataset. The developed tibial surface models and statistical shape model will be made available for use at: https://simtk.org/projects/ssm_tibia.

Tibial-fibular geometry and density variations associated with elevated bone strain and sex disparities in young active adults

Tibial stress fracture is a common injury in runners and military personnel. Elevated bone strain is believed to be associated with the development of stress fractures and is influenced by bone geometry and density. The purpose of this study was to characterize tibial-fibular geometry and density variations in young active adults, and to quantify the influence of these variations on finite element-predicted bone strain. A statistical appearance model characterising tibial-fibular geometry and density was developed from computed tomography scans of 48 young physically active adults. The model was perturbed ±1 and 2 standard deviations along each of the first five principal components to create finite element models. Average male and female finite element models, controlled for scale, were also generated. Muscle and joint forces in running, calculated using inverse dynamics-based static optimization, were applied to the finite element models. The resulting 95th percentile pressure-modified von Mises strain (peak strain) and strained volume (volume of elements above 4000 με) were quantified. Geometry and density variations described by principal components resulted in up to 12.0% differences in peak strain and 95.4% differences in strained volume when compared to the average tibia-fibula model. The average female illustrated 5.5% and 41.3% larger peak strain and strained volume, respectively, when compared to the average male, suggesting that sexual dimorphism in bone geometry may indeed contribute to greater stress fracture risk in females. Our findings identified important features in subject-specific geometry and density associated with elevated bone strain that may have implications for stress fracture risk.

Anatomically Parameterized Statistical Shape Model: Explaining Morphometry through Statistical Learning

Objective: Statistical shape models (SSMs) are a popular tool to conduct morphological analysis of anatomical structures which is a crucial step in clinical practices. However, shape representations through SSMs are based on shape coefficients and lack an explicit one-to-one relationship with anatomical measures of clinical relevance. While a shape coefficient embeds a combination of anatomical measures, a formalized approach to find the relationship between them remains elusive in the literature. This limits the use of SSMs to subjective evaluations in clinical practices. We propose a novel SSM controlled by anatomical parameters derived from morphometric analysis. Methods: The proposed anatomically parameterized SSM (ANAT_SSM) is based on learning a linear mapping between shape coefficients (latent space) and selected anatomical parameters (anatomical space). This mapping is learned from a synthetic population generated by the standard SSM. Determining the pseudo-inverse of the mapping allows us to build the ANAT_SSM. We further impose orthogonality constraints to the anatomical parameterization (OC-ANAT_SSM) to obtain independent shape variation patterns. The proposed contribution was evaluated on two skeletal databases of femoral and scapular bone shapes using clinically relevant anatomical parameters within each (five for femoral and six for scapular bone). Results: Anatomical measures of the synthetically generated shapes exhibited realistic statistics. The learned matrices corroborated well with the obtained statistical relationship, while the two SSMs achieved moderate to excellent performance in predicting anatomical parameters on unseen shapes. Conclusion: This study demonstrates the use of anatomical representation for creating anatomically parameterized SSMs and as a result, removes the limited clinical interpretability of standard SSMs. Significance: The proposed models could help analyze differences in relevant bone morphometry between populations, and be integrated in patient-specific pre-surgery planning or in-surgery assessment.

One size may not fit all: patient-specific computational optimization of locking plates for improved proximal humerus fracture fixation

BACKGROUND

Optimal treatment options for proximal humerus fractures (PHFs) are still debated because of persisting high fixation failure rates experienced with locking plates. Optimization of the implants and development of patient-specific designs may help improve the primary fixation stability of PHFs and reduce the rate of mechanical failures. Optimizing the screw orientations in locking plates has shown promising results; however, the potential benefit of subject-specific designs has not been explored yet. The purpose of this study was to evaluate by means of finite element (FE) analyses whether subject-specific optimization of the screw orientations in a fixed-angle locking plate can reduce the predicted cutout failure risk in unstable 3-part fractures.

METHODS

FE models of 19 low-density proximal humeri were generated from high-resolution computed tomographic images using a previously developed and validated computational osteosynthesis framework. The specimens were virtually osteotomized to simulate unstable malreduced 3-part fractures and fixed with the PHILOS plates using 6 proximal locking screws. The average principal compressive strain in cylindrical bone regions around the screw tips-a biomechanically validated surrogate for the risk of cyclic screw cutout failure-was defined as the main outcome measure. The angles of the 6 proximal locking screws were optimized via parametric analysis for each humerus individually, resulting in subject-specific screw orientations (SSO). The average peri-implant strains of the SSO were statistically compared with the previously reported cohort-specific (CSO) and original PHILOS screw orientations (PSO) for females vs. males.

RESULTS

The optimized SSO significantly reduced the peri-screw bone strain vs. CSO (6.8% ± 4.0%, P = .006) and PSO (25.24% ± 7.93%, P < .001), indicating lower cutout risk for subject-specific configurations. The benefits of SSO vs. PSO were significantly higher for women than men.

CONCLUSION

The findings of this study suggest that subject-specific optimization of the locking screw orientations could lead to lower cutout risk and improved PHF fixation. These computer simulation results require biomechanical and clinical corroboration. Further studies are needed to evaluate whether the potential benefit in stability could justify the increased efforts related to implementation of individualized implants. Nevertheless, computational exploration of the biomechanical factors influencing the outcome of fracture fixations could help better understand the fixation failures and reduce their incidence.

Automatic tracking of healthy joint kinematics from stereo-radiography sequences

Kinematic tracking of healthy joints in radiography sequences is frequently performed by maximizing similarities between computed perspective projections of 3D computer models and corresponding objects' appearances in radiographic images. Significant human effort associated with manual tracking presents a major bottleneck in biomechanics research methods and limits the scale of target applications. The current work introduces a method for fully-automatic tracking of tibiofemoral and patellofemoral kinematics in stereo-radiography sequences for subjects performing dynamic activities. The proposed method involves the application of convolutional neural networks for annotating radiographs and a multi-stage optimization pipeline for estimating bone pose based on information provided by neural net predictions. Predicted kinematics are evaluated by comparing against manually-tracked trends across 20 distinct trials. Median absolute differences below 1.5 millimeters or degrees for 6 tibiofemoral and 3 patellofemoral degrees of freedom demonstrate the utility of our approach, which improves upon previous semi-automatic methods by enabling end-to-end automation. Implementation of a fully-automatic pipeline for kinematic tracking will benefit evaluation of human movement by enabling large-scale studies of healthy knee kinematics.

Glenohumeral joint reconstruction using statistical shape modeling

Evaluation of the bony anatomy of the glenohumeral joint is frequently required for surgical planning and subject-specific computational modeling and simulation. The three-dimensional geometry of bones is traditionally obtained by segmenting medical image datasets, but this can be time-consuming and may not be practical in the clinical setting. The aims of this study were twofold. Firstly, to develop and validate a statistical shape modeling approach to rapidly reconstruct the complete scapular and humeral geometries using discrete morphometric measurements that can be quickly and easily measured directly from CT, and secondly, to assess the effectiveness of statistical shape modeling in reconstruction of the entire humerus using just the landmarks in the immediate vicinity of the glenohumeral joint. The most representative shape prediction models presented in this study achieved complete scapular and humeral geometry prediction from seven or fewer morphometric measurements and yielded a mean surface root mean square (RMS) error under 2 mm. Reconstruction of the entire humerus was achieved using information of only proximal humerus bony landmarks and yielding mean surface RMS errors under 3 mm. The proposed statistical shape modeling facilitates rapid generation of 3D anatomical models of the shoulder, which may be useful in rapid development of personalized musculoskeletal models.

Iliosacral screw corridors in Japanese subjects: a study using reconstruction CT scans

Objectives:

To investigate the characteristics of iliosacral (IS) screw corridors of Japanese pelves.

Methods:

Computer tomography images of 42 adult Japanese subjects without any pelvic injury were analyzed at a workstation. Using the manual reconstruction function, the width of a simulated horizontal corridor for an IS screw on the true coronal and true axial planes in the upper (S1), second (S2), and the third (S3) sacral segments was measured. For pelves without an adequate S1 corridor, a cranially tilted corridor was sought. A corridor was defined as “adequate” if its width on both planes was 10 mm or more.

Results:

An adequate horizontal corridor was found in S1 in 17 (40.5%) subjects, in S2 in 29 (69.0%) subjects, and in S3 in no subject. An independent factor affecting the adequacy of the S1 corridor was the adequacy of the S2 corridor (OR: 0.09). Similarly, an independent factor affecting S2 adequacy was S1 adequacy (OR: 0.10). A tilted, 10 mm diameter corridor was found in all 25 subjects who did not have an adequate horizontal corridor in the S1 segment. The angle required to obtain a 10 mm diameter corridor inversely correlated with the diameter of a horizontal corridor on the true coronal plane (R = −0.713, P = .000).

Conclusions:

The characteristics of IS screw corridors in the 42 Japanese subjects were similar to those reported in previous studies conducted in the West. The importance of preoperative planning using reliable techniques, such as three-dimensional reconstruction, should be emphasized.

Level of evidence: Diagnostic Level III. See Instructions for Authors for a complete description of level of evidence.

Machine learning for rapid estimation of lower extremity muscle and joint loading during activities of daily living

Joint contact and muscle forces estimated with musculoskeletal modeling techniques offer useful metrics describing movement quality that benefit multiple research and clinical applications. The expensive processing of laboratory data associated with generating these outputs presents challenges to researchers and clinicians, including significant time and expertise requirements that limit the number of subjects typically evaluated. The objective of the current study was to develop and compare machine learning techniques for rapid, data-driven estimation of musculoskeletal metrics from derived gait lab data. OpenSim estimates of patient joint and muscle forces during activities of daily living were simulated using laboratory data from 70 total knee replacement patients and used to develop 4 different machine learning algorithms. Trained machine learning models predicted both trend and magnitude of estimated joint contact (mean correlation coefficients ranging from 0.93 to 0.94 during gait) and muscle forces (mean correlation coefficients ranging from 0.83 to 0.91 during gait) based on anthropometrics, ground reaction forces, and joint angle data. Patient mechanics were accurately predicted by recurrent neural networks, even after removing dependence on key subsets of predictor features. The ability to quickly estimate patient mechanics from derived measurements of movement has the potential to broaden the impact of musculoskeletal modeling by enabling faster assessment in both clinical and research settings.

Investigating the Effects of Demographics on Shoulder Morphology and Density Using Statistical Shape and Density Modeling

A better understanding of how the shape and density of the shoulder vary among members of a population can help design more effective population-based orthopedic implants. The main objective of this study was to develop statistical shape models (SSMs) and statistical density models (SDMs) of the shoulder to describe the main modes of variability in the shape and density distributions of shoulder bones within a population in terms of principal components (PCs). These PC scores were analyzed, and significant correlations were observed between the shape and density distributions of the shoulder and demographics of the population, such as sex and age. Our results demonstrated that when the overall body sizes of male and female donors were matched, males still had, on average, larger scapulae and thicker humeral cortical bones. Moreover, we concluded that age has a weak but significant inverse effect on the density within the entire shoulder. Weak and moderate, but significant, correlations were also found between many modes of shape and density variations in the shoulder. Our results suggested that donors with bigger humeri have bigger scapulae and higher bone density of humeri corresponds with higher bone density in the scapulae. Finally, asymmetry, to some extent, was noted in the shape and density distributions of the contralateral bones of the shoulder. These results can be used to help guide the designs of population-based prosthesis components and pre-operative surgical planning.

Semi-supervised learning for automatic segmentation of the knee from MRI with convolutional neural networks

BACKGROUND AND OBJECTIVE

Segmentation is a crucial step in multiple biomechanics and orthopedics applications. The time-intensiveness and expertise requirements of medical image segmentation present a significant bottleneck for corresponding workflows. The current study develops and evaluates convolutional neural networks (CNNs) for automatic segmentation of magnetic resonance imaging (MRI) with the objective of assessing their utility for use in biomechanics research methods.

METHODS

CNNs were developed using a previously published, fully-annotated dataset as well as unlabeled scans from a publicly-available dataset. 2D and 3D CNNs were trained using semi-supervised learning frameworks for automatic segmentation of six structures of the knee. An inference strategy called Monte Carlo patch sampling was introduced to increase accuracy of the resulting models while adding no additional steps to the training process. Performance was assessed using traditional segmentation metrics, as well as surface error between reconstructed geometries from predicted and manual segmentations. Geometries from predicted segmentation maps were developed into finite element (FE) models in a semi-automatic pipeline and evaluated for FE-readiness.

RESULTS

3D CNNs using Monte Carlo patch sampling during inference achieved an Intersection-over-Union (IoU) of 0.978 and a dice similarity coefficient (DSC) of 0.989. Median surface error between predicted and ground truth geometries ranged from 0.56 to 0.98 mm. Meshes generated from the predicted segmentation maps were successfully used in FE simulations, demonstrating FE-readiness of geometries predicted by CNNs. CNNs trained with semi-supervised techniques outperformed CNNs trained in a fully-supervised fashion and resulted in performance competitive with similar literature despite relying on significantly less labeled data.

CONCLUSIONS

CNNs developed for automatic segmentation have potential for supplementing manual segmentation workflows in a wide range of orthopedics and biomechanics applications, including FE analysis. Faster processing times for developing FE models can enable population-based FE analysis using subject-specific models. The use of semi-supervised learning algorithms may additionally help circumvent the cost of obtaining labeled data in the development of these models.