Aims

Physician burnout and its consequences have been recognized as increasingly prevalent and important issues for both organizations and individuals involved in healthcare delivery. The purpose of this study was to describe and compare the patterns of self-reported wellness in orthopaedic surgeons and trainees from multiple nations with varying health systems.

Purpose. To achieve 3D kinematic analysis of total knee arthroplasty (TKA), 2D/3D registration techniques, which use X-ray fluoroscopic images and computer aided design model of the knee implants, have been applied to clinical cases. However, most conventional methods have needed time-consuming and labor-intensive manual operations in some process. In particular, for the 3D pose estimation of tibial component model from X-ray images, these manual operations were carefully performed because the pose estimation of symmetrical tibial component get severe local minima rather than that of unsymmetrical femoral component. In this study, therefore, we propose an automated 3D kinematic estimation method of tibial component based on statistical motion model, which is created from previous analyzed 3D kinematic data of TKA. Methods. The used 2D/3D registration technique is based on a robust feature-based (contour-based) algorithm. In our proposed method, a statistical motion model which represents average and variability of joint motion is incorporated into the robust feature-based algorithm, particularly for the pose estimation of tibial component. The statistical motion model is created from previous a lot of analyzed 3D kinematic data of TKA. In this study, a statistical motion model for relative knee motion of the tibial component with respect to the femoral component was created and utilized. Fig. 1 shows each relative knee motion model for six degree of freedom (three translations and three rotations parameter). Thus, after the pose estimation of the femoral component model, 3D pose of the tibial component model is determined by maximum a posteriori (MAP) estimation using the new cost function introduced the statistical motion model. Experimental results. To validate the feasibility and effectiveness of 3D pose estimation for the tibial component using the proposed method, experiments using X-ray fluoroscopic images of 20 TKA patients under the squatting knee motion were performed. For the creation of correct pose (reference data) and the statistical motion model, we used the 3D pose data which were got by carefully applying previous method to the contour images which spurious edges and noises were removed manually. In order to ensure the validity for the statistical motion model of the proposed method, leave-one-out cross validation method was applied. In the 3D pose estimation of tibial component model, for the only first frame, initial guess pose of the model was manually given. For all images except for the first frame, the 3D pose of the model was automatically estimated without manual initial guess pose of the model. To assess the automation performance, the automation rate was calculated, and the rate was defined as the X-ray frame number of satisfying clinical required accuracy (error within 1mm, 1 degree) relative to all X-ray frame number. As results of the experiments, 3D pose of the tibial component model for all X-ray images except for the first frame was full-automatically stably-estimated, and the automation rate was 80.1 %. Conclusions. The proposed method by MAP estimation introduced the statistical motion model was successfully performed, and did not need labor-intensive manual operations for 3D pose estimation of tibial component. For any figures or tables, please contact authors directly (see Info & Metrics tab above).

Slip and fall injuries represent a significant burden to the Canadian general public and healthcare system; the annual financial cost of these accidents in Canada is estimated to be $2 billion (2014). Interestingly, slip and fall accidents are not evenly distributed across the provinces, with the rate of hospitalization due to falls in Alberta being nearly three times greater than the rate in Ontario. Our research aim was to create the Alberta Slip and Fall Index (ASFI) – a simple scale like the UV or Air Quality index – that could be used to warn the general public about the presence of slippery conditions. The ASFI could be paired with interventions proven to prevent outdoor slips and falls, like promoting the use of ice cleats. Eleven years (January 2008 - December 2018) of emergency room presentations to the four adult hospitals in Calgary, Alberta were filtered based on the ICD-10 diagnostic code W00 (slip and fall due to ice and snow). Multivariable dispersion-corrected Poisson regression models were used to analyze the weather conditions and time of year most predictive of slip and fall injuries. A slip and fall risk calculator (the ASFI) was designed using output from statistical modelling. To validate the ASFI we compared model predicted slip and fall risk to real presentations using retrospective weather and patient data. The final dataset included 14,977 slip and fall incidents. The three months with the most emergency room presentations were January(n = 3591), February(n = 2997), and March(n = 2954); each of these predicted increased slip and fall accidents(p < 0 .001). Same day ice was significantly associated with more slip and fall accidents, as was the presence of ice one, two, and three days prior(p < 0 .001). Snow one day prior was mildly protective against slip and fall accidents, but this effect was not significant(p = 0.861). Snow, ice, and time of year variables can be input into the ASFI calculator, which computes the likelihood of slip and fall accidents on a 0-40 point scale, with 40 indicating maximum fall risk. Upon validation of the ASFI, we generally found days with the highest raw frequency of slip and fall accidents had higher ASFI scores. Although the ASFI can theoretically result in a score of 40, when we entered realistic weather conditions it was impossible to create a score higher than 20. The ASFI represents a tool that can be used to prevent slip and fall accidents due to icy and snowy conditions. As demonstrated by our inability to maximize the risk score when using realistic weather conditions, the ASFI is imperfect. Despite its shortcomings, the ASFI is a preliminary step towards effectively disseminating information about the weather conditions likely to lead to falls. Ideally, a refined ASFI will help people better understand when to use protective equipment and take extra precaution outdoors. If implementing the ASFI led to even a 1% decrease in injuries caused by falls, the annual Canadian healthcare savings would be roughly $2 million

Introduction. From 2004 to 2015, elective lumbar fusions increased by 62% in the US. The largest increases were for among age 65 or older (139% in volume) and scoliosis (187%) [1]. Age is a well known factor of osteoporosis. The load-sharing may exceed the pedicular screws constructs in aging spine and lead to non-union and re-do. Surgical options may increase the screw purchase (e.g.: augmentation, extensions) at supplementary risks. Pedicular screw are known to cause vascular, nerve root or cord injuries. Facing these pitfalls, the surgeon's experience and rule of thumbs are the most deciding factors for the surgical planning. The aim of this study is to assess the accuracy of a patient specific tool, designed to plan a safe pedicular trajectory and to provide an intraoperative screw pullout strength estimate. Materials and Methods. Clinical QCT were taken for nine cadaveric spines (82 y. [61; 87], 6 females, 3 males). The experimental maximum axial pullout resistance (FMax) of twenty-seven pedicular screws inserted (nine T12, nine L4 and nine L5) was obtained as described in a previous study [2]. A custom 3D-WYSIWYG software simulated a medio-lateral surgical insertion technique in the QCTs coordinates reference, respecting the cortical walls. Repeatable density, morphometric and hardware parameters were recorded for each vertebrae. A statistical model was built to match predictive and experimental data. Preliminary results. Experimental FMax(N) were [104;953] (359 ±223). A further displacement of 1,81mm ±0,35 halved the experimental FMax. Predictive FMax(N) were [142;862] (359 ±220). A high positive correlation between experimental and predictive FMax was revealed (Pearson, ρ = 0.93, R2 = 0.87, p < .001, figure 1). Absolute differences ranged between 3N and 177N. Discussion. A high screw purchase in primary fixation is paramount to achieve spine surgical procedures (e.g.: kyphosis, scoliosis) and postoperative stability for vertebrae fusion. High losses of screw purchase by bone plastic deformation, begin with tiny pullouts. Theses unwanted intraoperative millimetric over-displacements are hard to avoid when monitoring at the same time tens of screws surrounded by bleedings. This advocates for including predictive FMax for each implantable pedicular screw in the surgical planning decision making process to prevent failures and assess risks. For the first time, this study presents an experimentally validated statistical model for FMax prediction with a safe trajectory definition tool, including patients’ vertebrae and hardware properties and referring to the patient's clinical 3D quantitative imagery. The model was able to differentiate between bone quality and vertebrae variations. More extensive model validation is currently ongoing to interface with robotics & navigation systems and to produce meshes for 3D printing of sterilizable insertion guides

Bone shape estimation from partial observations, such as fluoroscopy or ultrasound, has been subject of significant interest over the past decade and can be regarded as the driving force behind several advances in statistical modelling of shape. While statistical models were initially used mostly as regularisers constraining shape matching algorithms, they are now increasingly employed due to their predictive ability, when only limited observations are available. With the current efforts toward minimal invasiveness, radiation exposure reduction, and optimization of the cost-effectiveness of procedures, two major challenges emerge on the field of statistical modelling. The first one is to develop methods that enable the use of as much information as possible that can be relevant for a specific shape prediction task, within the aforementioned limits. The second challenge concerns the accuracy of the resulting predictions, which needs to be quantified in order to evaluate the associated risks, and to optimise the data acquisition procedures. In terms of shape prediction, most studies so far have concentrated on individualizing statistical atlases based on imaging data. However, relevant information about skeletal morphology can also be obtained from simple anthropometric and morphometric measurements such as gender, age, body-mass index, and bone specific measurements. We develop a multivariate regression framework that enables to take into account such combinations of predictors simultaneously with sparse observations of the bone surface for improved prediction of the complete bone shape. In particular, we describe in a quantitative and localised fashion the individual contributions but also the complementarities of the heterogeneous sources of information with respect to bone morphology assessment. To do so, we compare the prediction errors obtained with different combinations of predictors, relying on cross-validation experiments. In addition to providing valuable and complementary predictive information, non-imaging measurements can be exploited to automatically initialise surface registration algorithms which increase their robustness for the determination of patient specific morphologies. A statistical model, by essence, is a mathematical model resulting from a learning phase using a set of training data. Statistical model based prediction is affected by three main sources of errors. The pre-processing of the training data, in particular the establishment of anatomical correspondences between the different samples, and the limited number of training elements constitute a first source of uncertainties. Second, the predictors can be affected by measurement noise, which will then propagate through the prediction process. Finally, and this is particularly important in the context of sparse observation data, the limited correlations between the predictors and the shape to predict imply theoretical limits for the achievable accuracy of such approaches. We have developed a framework enabling to account for these various sources of uncertainty, and propagating them through the prediction pipeline to generate confidence regions around the predicted shape. It relies extensively on cross-validation experiments in order to quantify the limitations of the statistical model with respect to the representation of new shapes (generalization ability) and to their prediction from partial data. Furthermore, we demonstrate the reliability of the obtained regions, following the procedure initially proposed in. We evaluate our approaches on a database of 140 femur bones, age range: 23–83, mean 62.57, stdv 15; 46% males and 54% females, with known age, height and weight. Morphometric measurements such as bone length, inter-condyle distance or anteversion angle are considered, either as predictors, together with sparse point clouds around the femoral head and greater trochanter, or as a pose-independent quality-of-fit metric. Cross-validation experiments indicate that a higher accuracy can be reached when complementing surface-based predictors with relevant anthropometric and morphometric information, and that reliable confidence regions can be estimated

Tibial pilon fractures are typically the result of high-energy axial loads, with complex intra- articular fractures that are often difficult to reconstruct anatomically. Only nine simultaneous pilon and talus fractures have been published previously, but we hypothesised the chondral surface of the dome is affected more frequently. Data was acquired prospectively from 154 acute distal tibial pilon fractures (AO/OTA 43B/C) in adults. Radiographs, photographs, and intra-operative drawings of each case were utilised to document the presence of any macroscopic injuries of the talus. Detailed 1x1mm maps were created of the injuries in each case and transposed onto a statistical shape model of a talus; this enables the cumulative data to be analysed in Excel. Data was analysed using a Chi-squared test. From 154 cases, 104 were considered at risk and their talar domes were inspected; of these, macroscopic injuries were identified in 55 (52.4%). The prevalence of talar dome injury was greater with B-type fractures (53.5%) than C-type fractures (31.5%) (ρ = .01). Injuries were more common in men than women and presented with different distribution of injuries (ρ = .032). A significant difference in the distribution of injuries was also identified when comparing falls and motor vehicle accidents (ρ = .007). Concomitant injuries to the articular surface of the dome of the talus are relatively common, and this perhaps explains the discordance between the post-operative appearance following internal fixation and the clinical outcomes observed. These injuries were focused on the lateral third of the dome in men and MVAs, whereas women and fall mechanism were more evenly distributed. Surgeons who operatively manage high-energy pilon fractures should consider routine inspection of the talar dome to assess the possibility of associated macroscopic osteochondral injuries

Knowledge of the premorbid glenoid shape and the morphological changes the bone undergoes in patients with glenohumeral arthritis can improve surgical outcomes in total and reverse shoulder arthroplasty. Several studies have previously used scapular statistical shape models (SSMs) to predict premorbid glenoid shape and evaluate glenoid erosion properties. However, current literature suggests no studies have used scapular SSMs to examine the changes in glenoid surface area in patients with glenohumeral arthritis. Therefore, the purpose of this study was to compare the glenoid articular surface area between pathologic glenoid cavities from patients with glenohumeral arthritis and their predicted premorbid shape using a scapular SSM. Furthermore, this study compared pathologic glenoid surface area with that from virtually eroded glenoid models created without influence from internal bone remodelling activity and osteophyte formation. It was hypothesized that the pathologic glenoid cavities would exhibit the greatest glenoid surface area despite the eroded nature of the glenoid and the medialization, which in a vault shape, should logically result in less surface area. Computer tomography (CT) scans from 20 patients exhibiting type A2 glenoid erosion according to the Walch classification [Walch et al., 1999] were obtained. A scapular SSM was used to predict the premorbid glenoid shape for each scapula. The scapula and humerus from each patient were automatically segmented and exported as 3D object files along with the scapular SSM from a pre-operative planning software. Each scapula and a copy of its corresponding SSM were aligned using the coracoid, lateral edge of the acromion, inferior glenoid tubercule, scapular notch, and the trigonum spinae. Points were then digitized on both the pathologic humeral and glenoid surfaces and were used in an iterative closest point (ICP) algorithm in MATLAB (MathWorks, Natick, MA, USA) to align the humerus with the glenoid surface. A Boolean subtraction was then performed between the scapular SSM and the humerus to create a virtual erosion in the scapular SSM that matched the erosion orientation of the pathologic glenoid. This led to the development of three distinct glenoid models for each patient: premorbid, pathologic, and virtually eroded (Fig. 1). The glenoid surface area from each model was then determined using 3-Matic (Materialise, Leuven, Belgium). Figure 1. (A) Premorbid glenoid model, (B) pathologic glenoid model, and (C) virtually eroded glenoid model. The average glenoid surface area for the pathologic scapular models was 70% greater compared to the premorbid glenoid models (P < 0 .001). Furthermore, the surface area of the virtual glenoid erosions was 6.4% lower on average compared to the premorbid glenoid surface area (P=0.361). The larger surface area values observed in the pathologic glenoid cavities suggests that sufficient bone remodelling exists at the periphery of the glenoid bone in patients exhibiting A2 type glenohumeral arthritis. This is further supported by the large difference in glenoid surface area between the pathologic and virtually eroded glenoid cavities as the virtually eroded models only considered humeral anatomy when creating the erosion. For any figures or tables, please contact the authors directly

Percutaneous fixation of scaphoid fractures has become popular in recent years, mainly due to its reduced complexity compared to open surgical approaches. Fluoroscopy is currently used as guidance for this percutaneous approach, however, as a projective imaging modality, it provides only a 2D view of the complex 3D anatomy of the wrist during surgery, and exposes both patient and physician to harmful X-ray radiation. To avoid these drawbacks, 3D ultrasound has been suggested to provide imaging for guidance as a widely available, real-time, radiation-free and low-cost modality. However, the blurred, disconnected, weak and noisy bone responses render interpretation of the US data difficult so far. In this work, we present the integration of 3D ultrasound with a statistical wrist model to allow development of an improved ultrasound-based guidance procedure. For enhancement of bone responses in ultrasound, a phase symmetry based approach is used to exploit the symmetry of the ultrasound signal around the expected bone location. We propose an improved estimation of the local phase symmetry by using the local spectrum variation of the ultrasound image. The statistical wrist model is developed through a group-wise registration based framework in order to capture the major modes of shape and pose variations across 30 subjects at different wrist positions. Finally, the statistical wrist model is registered to the enhanced ultrasound bone surfaces using a probabilistic registration approach. Feasibility experiments are performed using two volunteer wrists, and the results are promising and warrant further development and validation to enable ultrasound guided percutaneous scaphoid fracture reduction

Introduction:. Leg length and offset discrepancy resulting from Total Hip Replacement (THR) is a major cause of concern for the orthopaedic community. The inability to substitute the proximal portion of the native femur with a device that suitably mimics the pre-operative offset and head height can lead to loss of abductor power, instability, lower back pain and the need for orthodoses (1). Contemporary devices are manufactured based on predicate studies (2–4) to cater for the variations within the patient demographic. Stem variants, modular necks and heads are often provided to meet this requirement. The number of components and instruments that manufacturers are prepared to supply however is limited by cost and an unwillingness to introduce unnecessary complexity. This can restrict their ability to achieve the pre-osteoarthritic head centre for all patient morphologies. Corin has developed bone conserving prosthesis (MiniHip™) to better replicate the physiological load distribution in the femur. This study assesses whether the MiniHip™ prosthesis can better match the pre-osteoarthritic head centre for patient demographics when compared to contemporary long stem devices. Method:. The Dorr classification is a well accepted clinical method for defining femoral endosteal morphology (5). This is often used by the surgeon to select the appropriate type and size of stem for the individual patient. It is accepted that a strong correlation exists between Flare Index (FI), characterising the thinning of cortical walls and development of ‘stove-pipe’ morphology, and age, in particular for females (Table 1) (3). A statistical model of the proximal femur was built from 30 full length femoral scans (Imorphics, UK). Minimum and maximum intramedullary measurements calculated from the statistical model were applied to relationships produced by combining Corins work with that of prior authors (Table 2) (2; 3; 6). This data was then used to generate 2D CAD models into which implants were inserted to compare the head centres achievable with a MiniHip™ device compared to those of a contemporary long stem. Results:. Results for the CAD overlay indicated the MiniHip prosthesis is better suited to restoring head centre for a range of morphological variations (Figure 1). In contrast, the long stem prosthesis requires a larger size range and increased inventory in terms of stem variants and modular components to achieve the same array of head centres. The disparity between the Corin FI and that of prior authors can be accounted for by the methods employed; the greyscale-based edge detection (Imorphics, UK) compared to a manual identification method. Discussion:. By overlaying the Corin MiniHip™ over the CAD representation of anticipated flare index, it is evident that the MiniHip™ stem is more suitable for the anticipated range of morphologies. The versatility of this design enables the restoration of head height and offset regardless of canal geometry, degree of offset and or CCD angle. This is not the case for contemporary long stem devices which rely on a more diaphyseal region for anchorage and stability and therefore depend on stem variants and modularity to cater for morphology changes

Background. Aseptic loosening is the leading cause of total knee arthroplasty (TKA) failure in the long term, of which osteolysis from polyethylene wear debris remains a problem that can limit the lifetime of TKA past the second decade. To help speed up design innovations, our goal was to develop a computational framework that could efficiently predict the effect of many sources of variability on TKA wear—including design, surgical, and patient variability. Methods. We developed a computational framework for predicting TKA contact mechanics and wear. The framework accepts multiple forms of input data: patient-specific, population-specific, or standardized motions and forces. CAD models are used to create the FEA mesh. An analytical wear model, calibrated from materials testing (wheel-on-flat) experiments, is fully integrated into the FEA process. Isight execution engine runs a design of experiments (DOE) analysis with an outcome variable, such as volumetric wear, to guide statistical model output. We report two DOE applications to test the utility of the computational framework for performing large variable studies in an efficient manner: one to test the sensitivity of TKA wear to the femoral center of rotation, and the second to test the sensitivity of TKA wear to gait input perturbations. Results. Using this method, we demonstrated that choice of femoral center of rotation matters, and that although volumetric wear was most sensitive to variation in flexion/extension peaks, no one kinematic factor dominates TKA volumetric wear variability. Conclusion. The two DOE applications represent initial first attempts to study variability in component alignment and input waveforms across large solution spaces. The computational framework will be most useful if it can be used in a TKA design setting, where new innovations can be tested as soon as they are developed to see if they are worthy of further mechanical testing

Introduction. Navigation in total hip arthroplasty (THA) has the goal to improve accuracy of cup orientation. Measurement of cup orientation on conventional pelvic radiographs is susceptible to error due to pelvic malpositioning during acquisition. A recently developed and validated software using a postoperative radiograph in combination with statistical shape modelling allows calculation of exact 3-dimensional cup orientation independent of pelvic malpositioning. Objectives. We asked (1) what is the accuracy of computer-navigated cup orientation (inclination and anteversion) and (2) what is the percentage of outliers (>10° difference to aimed inclination and anteversion) using postoperative measurement of 3-dimensional cup orientation. Methods. We performed a retrospective comparative study including a single surgeon series with 114 THAs (109 patients). Surgery was performed through the anterolateral approach with the patient in supine position. An image-free navigation system (PiGalileo, Smith & Nephew) with a passive digital reference base for the pelvic wing and one for the distal femur was used. The anterior pelvic plane (APP) was registered manually using a pointer and used as anatomical reference. After implantation of the press-fit cup (EP-Fit plus, Smith & Nephew) the final cup orientation (inclination and anteversion) was registered with the navigation system. Postoperative orientation was calculated using validated software to calculate 3-dimensional cup orientation. The postoperative anteroposterior pelvic radiograph in combination with a statistical model of the pelvis allowed calculation of inclination and anteversion referenced to the APP. The software was previously validated using CT measurements and revealed a mean accuracy of 0.4° for inclination 0.6° for anteversion with a maximum error of 3.3° and 3.6°, respectively. The mean postoperative inclination in the current series was 46° ± 4° (range, 35° – 60°) and the mean anteversion was 23° ± 6° (range, 11° – 37°). Accuracy was calculated as the absolute difference of the intraoperative registered cup orientation and the postoperative calculated orientation. An outlier was defined if cup orientation was outside a range of ±10° of inclination and/or anteversion. Results. (1) The mean accuracy for inclination was 3 ± 3° (0 – 17°) and 6 ± 5° (0 – 22°) for anteversion. (2) Three out of 114 cups (3%) were outliers for inclination. An increased percentage of outliers was found for anteversion with 23 out of 114 cups (20%; p<0.001). In total, 25 cups (22%) were outliers (See Figure 1). Conclusions. Previous studies evaluating accuracy of cup orientation were limited in numbers of hips due to the use of CT or used measurements on conventional postoperative radiographs which are prone to error due to pelvic malpositioning. Novel and validated software allows accurate and anatomically referenced measurement of postoperative cup orientation. This study is the single largest case series with 3-dimensional measurement of cup orientation for validation of navigated THA. Computer-assisted image-free navigation of cup orientation showed a high accuracy of cup orientation with 78% within a narrow range of ±10° of inclination and anteversion. Accuracy of cup inclination was increased compared to cup anteversion. For any figures or tables, please contact authors directly (see Info & Metrics tab above).

Introduction & aims. Satisfaction following total knee replacement (TKR) surgery remains suboptimal at around 80%. Prediction of factors influencing satisfaction may help manage expectations and thus improve satisfaction. We investigated preoperative variables that estimate the probability of achieving a successful surgical outcome following TKR in several outcomes important to patients. Method. 9 pre-operative variables (easily obtained on initial consultation) of 591 unilateral TKRs were selected for univariant then multivariant analyses. These variables included Oxford Knee Score (OKS), age, sex, BMI, ASA score, pain score, mobility aids, SF12 PCS & SF12 MCS. Using the relative predictive strengths of these variables we modeled the probabilities a successful result would be achieved for 6 patient reported outcomes at 3 and 12 months following surgery. These were ‘Excellent/good OKS’, ‘Mild/no pain’, ‘Walking without/at first a limp’, ‘No/little interference with normal work’, ‘Kneeling with little/no difficulty’ and ‘Satisfaction with surgery’. Results. Pre-operative OKS was the most useful single predictor, having impact at three months and/or one year on all outcomes examined, except kneeling. SF12 MCS affected pain scores, pain with usual activity, and limp at three months and/or one year. At three months, BMI, age, gender, ASA and pain also influenced one or more of 6 post-operative outcomes studied. After inputting pre-operative OKS, adding other predictors did not significantly improve the statistical model. Conclusions. Our model provides objective probability estimates based on the outcomes of our previous TKRs, which we can use to give specific objective information to prospective TKR patients regarding their likely postoperative trajectory. We hope this will modulate patient expectations, assist preparation for their surgical experience and in turn increase satisfaction

Background. Artificial total knee designs have revolutionized over time, yet 20% of the population still report dissatisfaction. The standard implants fail to replicate native knee kinematic functionality due to mismatch of condylar surfaces and non-anatomically placed implantation. (Daggett et al 2016; Saigo et al 2017). It is essential that the implant surface matches the native knee to prevent Instability and soft tissue impingement. Our goal is to use computational modeling to determine the ideal shapes and orientations of anatomically-shaped components and test the accuracy of fit of component surfaces. Methods. One hundred MRI scans of knees with early osteoarthritis were obtained from the NIH Osteoarthritis Initiative, converted into 3D meshes, and aligned via an anatomic coordinate system algorithm. Geomagic Design X software was used to determine the average anterior-posterior (AP) length. Each knee was then scaled in three dimensions to match the average AP length. Geomagic's least-squares algorithm was used to create an average surface model. This method was validated by generating a statistical shaped model using principal component analysis (PCA) to compare to the least square's method. The averaged knee surface was used to design component system sizing schemes of 1, 3, 5, and 7 (fig 1). A further fifty arthritic knees were modeled to test the accuracy of fit for all component sizing schemes. Standard deviation maps were created using Geomagic to analyze the error of fit of the implant surface compared to the native femur surface. Results. The average shape model derived from Principal Component Analysis had a discrepancy of 0.01mm and a standard deviation of 0.05mm when compared to Geomagic least squares. The bearing surfaces showed a very close fit within both models with minimal errors at the sides of the epicondylar line (fig 2). The surface components were lined up posteriorly and distally on the 50 femurs. Statistical Analysis of the mesh deviation maps between the femoral condylar surface and the components showed a decrease in deviation with a larger number of sizes reducing from 1.5 mm for a 1-size system to 0.88 mm for a 7-size system (table 1). The femoral components of a 5 or 7-size system showed the best fit less than 1mm. The main mismatch was on the superior patella flange, with maximum projection or undercut of 2 millimeters. Discussion and Conclusion. The study showed an approach to total knee design and technique for a more accurate reproduction of a normal knee. A 5 to 7 size system was sufficient, but with two widths for each size to avoid overhang. Components based on the average anatomic shapes were an accurate fit on the bearing surfaces, but surgery to 1-millimeter accuracy was needed. The results showed that an accurate match of the femoral bearing surfaces could be achieved to better than 1 millimeter if the component geometry was based on that of the average femur. For any figures or tables, please contact the authors directly

The objective of our study is to evaluate the accuracy of an X-ray based image segmentation system for patient specific instrument (PSI) design or any other surgical application that requires 3D modeling of the knee. The process requires two bilateral short film X-ray images of knee and a standing long film image of the leg including the hip and ankle. The short film images are acquired with an X-ray positioner device that is embedded with fiducial markers to correct for setup variation in source and cassette position. An automated image segmentation algorithm, based on a statistical model that couples knee bone shape and radiographic appearance, calculates 3D surface models of the knee from the bi-lateral short films (Imorphics, Manchester UK) (Figure 1). Surface silhouettes are used to inspect and refine the automatically generated segmentation; the femur and tibia mechanical axes are then calculated using automatically generated surface model landmarks combined with user-defined markups of the hip and ankle center from the standing long film (Figure 2). The accuracy of the 2D/3D segmentation system was evaluated using simulated X-ray imagery generated from one-hundred osteoarthritic, lower limb CT image samples using the Insight Toolkit (Kitware, Inc.). Random, normally distributed variations in source and cassette positions were included in the dataset. Surface accuracy was measured using root-mean-square (RMS) point-to-surface (P2S) distance calculations with respect to paired benchmark CT segmentations. Landmark accuracy was calculated by measuring angular differences between the 2D/3D generated femur and tibia mechanical tibia with respect to paired CT-generated landmark data. The paired RMS sample mean and standard deviation of femur P2S errors on the distal quarter of the femur after auto-segmentation was 1.08±0.20mm. The RMS sample mean and standard deviation of tibia P2S errors on the proximal quarter of the tibia after auto-segmentation was 1.16±0.25mm. The paired sample mean and standard deviation of the femur and tibia mechanical axis accuracy with respect to benchmark CT data landmarks were 0.02±0.42[deg] and −0.33±0.56[deg], respectively. Per surface-vertex sample RMS P2S errors are illustrated in Figure 3. Visual inspection of RMS results found the automatically segmented femur to be very accurate in the shaft, distal condyles, and posterior condyles, which are important for PSI guide fit and accurate planning. Similarly, the automatically segmented tibia was very accurate in the shaft and plateaus, which are also important for PSI guide fit. Osteophytes resulted in some RMS differences (Figure 3), as was expected due to the know limitations of osteophyte imaging with X-ray. PSI-type applications that utilize X-ray should account for osteophyte segmentation error. Overall, our results based on simulated radiographic data demonstrate that X-ray based 2D/3D segmentation is a viable tool for use in orthopaedic applications that require accurate 3D segmentations of knee bones

Statistical shape modeling (SSM) and statistical density modeling (SDM) are tools capable of describing the main modes of deviation in the shape and density distribution of the shoulder using a set of uncorrelated variables called principal components (PCs). We hypothesize that the first PC of the SDM, which scales overall density up/down, will be inversely correlated with age and will, on average, be greater for males than females. We also hypothesize that there is a correlation between some PCs of shape and density. SSM and SDM were developed for scapulae and humeri by segmenting surface meshes from computed tomographic images of 75 cadaveric shoulders. Bones were co-registered and defined by the same surface mesh. Volumetric tetrahedral meshes were defined for one of the specimens serving as base meshes for SDM. Base meshes were morphed to each individual bone's surface and superimposed upon the corresponding CT data to determine image intensity in Hounsfield units at each node. Principal component analysis was performed on the exterior shape and internal density distribution of bones. T-tests were performed to find any differences in PC scores between males and females, and Pearson correlation coefficients were calculated for age and PC scores. Finally, correlation coefficients between each of the PCs of the shape and density models were calculated. For the humerus, the first three PCs of the SDM were significantly correlated with age (ρ = 0.40, −0.46, and 0.36, all p ≤ 0.007). For the scapula, the first and ninth PCs showed such correlation (ρ = −0.31, and −0.32, all p ≤ 0.02). Statistically significant differences due to sex were found for the second to sixth SDM PCs of the humerus, with differences in average PC scores of 1, 1, −0.7, −0.8, and −0.6 standard deviations, respectively, for males relative to females. For the scapula, the second, fifth and seventh SDM PCs were significantly different between males and females, with average PC scores differing by 1.1, 0.7, and −0.6 standard deviations. Finally, for both bones, the first PC of SSM showed a weak but significant correlation with the second PC of the SDM (ρ = 0.47, p < 0.001 for the humerus, and ρ = 0.39, p < 0.001 for the scapula). The results of this study suggest that age has a significant influence on the first PC of the SDM, associated with scaling the density in the cortical boundary. Moreover, the negative correlation of age with the second PC of the humerus in SDM which mostly influences the thickness of the cortical boundary implies cortical thinning with age. The second PC of both bones differed significantly between males and females, implying that cortical thickness differs between sexes. Also, there was a significant correlation between the size of the bones and the thickness of the cortical boundary. These findings can help guide the designs of population-based prosthesis components

Introduction. Obtaining accurate anatomical landmarks may lead to a better morphologic understanding, but this is challenging due to the variation of bony geometries. A manual approach, non-ideal for surgeons or engineers, requires a CT or MRI scan, and landmarks must be chosen based on the 3D representation of the scanned data. Ideally, anatomical landmarking is achieved using either a statistical shape model or template matching. Statistical modeling approaches require multitude of training data to capture population variation. Prediction of anatomical landmarks through template matching techniques has also been extensively investigated. These techniques are based on the minimization or maximization of an objective or cost function. As is the nature of non-rigid algorithms, these techniques can fail in the local maxima if the template and new bone models have noise or outliers. Therefore, a combination of rigid and non-rigid registration techniques is needed, in order to obtain accurate anatomical landmarks and improve the prediction process. Objective. The objective of this study was to find a way to efficiently obtain accurate anatomical landmarks based on an existing template's landmarks for use in a forward solution model (FSM) to predict patient specific mechanics. Methods. Initially, the 3D meshes for a template bone and new bone of question are imported into the FSM. Landmarks on the template are also loaded with imported data. Then, the template and new bones are located at arbitrary positions within the global coordinate system. If determined to be placed at significantly different positions, the user will re-align the bones to ensure that they are close enough for the process to commence. After initially aligning the bones, the new bone model will appear closer to the template. The template bone model is then registered to the new model using Iterative Closest Point (ICP) with scaling to find the initial regions of correspondence. For each anatomical landmark on the template, initial corresponding landmarks on the new bone are defined as being its closest point. To refine landmarks on the new bone, local corresponding regions are determined between the template and new bone models. Local corresponding regions on the template and new bone models are then registered again using ICP with a scaling algorithm to refine the landmark locations on the new model as seen in Figure 1. Results. Regardless of differences in size, geometry, and initial position, the algorithm has proven to be successful in transferring landmarks from the template bone to the new bone model (Figure 2). The results also revealed that predicted landmarks on the new bone (purple) are properly defined with respect to the landmarks on the template bone (green) (Figure 2). This process allows for the FSM to be parametric in nature for patient specific analyses. Discussion and Conclusion. The FSM successfully transferred anatomical landmarks from a template to a new bone model. It has also been proven to work on more than just the femur and pelvis. Future investigations using this process for surgical planning/implant sizing will be used for both our hip and knee FSMs. For any figures or tables, please contact authors directly

Introduction. Revision total hip arthroplasty is often associated with acetabular bone defects. In most cases, assessment of such defects is still qualitative and biased by subjective interpretations. Three-dimensional imaging techniques and novel anatomical reconstructions using statistical shape models (SSM) allow a more impartial and quantitative assessment of acetabular bone defects [1]. The objectives of this study are to define five clinically relevant parameters and to assess 50 acetabular bone defects in a quantitative way. Methods. Anonymized CT-data of 50 hemi-pelvises with acetabular bone defects were included in the study. The assessment was based on solid models of the defect pelvis (i.e. pelvis with bone defect) and its anatomical reconstruction (i.e. native pelvis without bone defect) (Fig.1A). Five clinically relevant parameters were defined: (1) Bone loss, defined by subtracting defect pelvis from native pelvis. (2) Bone formation, defined by subtracting native pelvis from defect pelvis. Bone formation represents bone structures, which were not present in the native pelvis (e.g. caused by remodeling processes around a migrated implant). (3) Ovality, defined by the length to width ratio of an ellipse fitted in the defect acetabulum. A ratio of 1.0 would represent a circular acetabulum. (4) Lateral center-edge angle (LCE angle), defined by the angle between the most lateral edge of the cranial roof and the body Z-axis, and (5) implant migration, defined by the distance between center of rotation (CoR) of the existing implant and CoR of native pelvis (Fig. 1B). Results. All data are presented as single values as well as median and [25. th. , 75. th. ]- percentile (Fig.2). Bone loss was 53.6 [41.5, 76.7] ml with a minimum of 19.0 ml and maximum of 103.9 ml. Bone formation was 15.7 [10.5, 21.2] ml with a minimum of 3.5 ml and a maximum of 41.6 ml. Ovality was 1.3 [1.1, 1.4] with a minimum of 1.0 and a maximum of 2.0. LCE angle was 30.4° [21.5°, 40.1°] with a minimum of 11.6° and a maximum of 63.0°. Implant migration was 25.3 [15.1, 32.6] mm with a minimum of 5.4 mm and a maximum of 53.5 mm. Discussion. Within this study, 50 hemi-pelvises with acetabular bone defects were successfully quantified using five clinically relevant parameters. Application of this method provides impartial and quantitative data of acetabular bone defects, which could be beneficial in clinical practice for pre-operative planning or comparison of surgical outcomes. Including a larger number of cases, this method could even serve as a basis for a novel classification system for acetabular bone defects. For any figures or tables, please contact the authors directly

Introduction:. Primary stability is crucial for long-term fixation of cementless tibial trays. Micromotion less than 50 μm is associated with stable bone ingrowth and greater than 150 μm causes the formation of fibrous tissue around the implant [1, 2]. Finite element (FE) analysis of complete activities of daily living (ADL's) have been used to assess primary stability, but these are computationally expensive. There is an increasing need to account for both patient and surgical variability when assessing the performance of total joint replacement. As a consequence, an implant should be evaluated over a spectrum of load cases. An alternative approach to running multiple FE models, is to perform a series of analyses and train a surrogate model which can then be used to predict micromotion in a fraction of the time. Surrogate models have been used to predict single metrics, such as peak micromotion. The aim of this work is to train a surrogate model capable of predicting micromotion over the entire bone-implant interface. Methods:. A FE model of an implanted proximal tibia was analysed [3] (Fig. 1). A statistical model of knee kinetics, incorporating subject-specific variability in all 6-DOF joint loads [4], was used to randomly generate loading profiles for 50 gait cycles. A Latin Hypercube (LH) sampling method was applied to sample 6-DOF loads of the new population throughout the gait cycle. Kinetic data was sampled at 10, 50 and 100 instances and FE predictions of micromotion were calculated and used to train a surrogate model capable of describing micromotion over the entire bone-implant interface. The surrogate model was tested for an unseen gait cycle and the resulting micromotions were compared with FE predictions. Results and discussion:. Accuracy of the surrogate model increased with increasing sample size in the training set; with a LH sample of 10, 50 and 100 trials, the surrogate model predicted micromotion at the bone-implant interface during gait with RMS accuracy of 61, 44 and 33 μm, respectively (Fig. 2). Similar range in micromotion was measured in FE and surrogate models; although the surrogate model tended to over-predict micromotion early in the gait cycle (Fig. 2). There was good agreement in location and magnitude of micromotion at the interface surface through out the gait cycle (Fig. 3). Although encouraging, further work is required to optimize the number and distribution of the training samples to minimize the error in the surrogate model. Analysis time for the FE model was 15 hours, compared to 30 seconds for the surrogate model. The results suggest that surrogate models have significant potential to rapidly predict micromotion over the entire bone-implant interface, allowing for a greater range in loading conditions to be explored than would be possible through conventional methods

Introduction. Problematic bone defects are encountered regularly in orthopaedic practice particularly in fracture non-union, revision hip and knee arthroplasty, following bone tumour excision and in spinal fusion surgery. At present the optimal source of graft to ‘fill’ these defects is autologous bone but this has significant drawbacks including harvest site morbidity and limited quantities. Bone marrow has been proposed as the main source of osteogenic stem cells for the tissue-engineered cell therapy approach to bone defect management. Such cells constitute a minute proportion of the total marrow cell population and their isolation and expansion is a time consuming and expensive strategy. In this study we investigated human bone marrow stem cells as a potential treatment of bone defect by looking at variability in patient osteogenic cell populations as a function of patient differences. We produced a model to predict which patients would be more suited to cell based therapies and propose possible methods for improving the quality of grafts. Methods. Bone marrow was harvested from 30 patients undergoing elective total hip replacement surgery in Musgrave Park Hospital, Belfast (12 males, 18 females, age range 52-82 years). The osteogenic stem cell fraction was cultured and subsequently analysed using colony forming efficiency assays, flow cytometry, fluorescence activated cell sorting and proteomics. Results. The number and proliferative capacity of osteogenic stem cells varied markedly between patients. Statistical analysis revealed significantly better osteogenic capacity in:. male patients. samples in which the growth hormone Fibroblastic Growth Factor-2 was added to culture medium. patients who used the cholesterol lowering agent simvastatin. Patient use of inhaled steroids and NSAIDs were found to have detrimental effects. A statistical model to predict marrow profiles based on these variables was produced. Conclusions. Stem cell based tissue engineering represents the future of the treatment of bone defect. This study provides evidence that inter-patient variability in marrow cell colony forming and proliferation ability can in some way be explained by patient associated factors. Using this knowledge, we can identify which patients would be best suited to this method of treatment and propose techniques for enhancement of their graft profiles

INTRODUCTION. Automated MRI bone segmentation is one of the most challenging problems in medical imaging. To increase the segmentation robustness, a prior model of the structure could guide the segmentation. Statistical Shape Models (SSMs) are efficient examples for such application. We present an automated SSM construction approach of the scapula bone with an adapted initialisation to address the correspondences problem. Our innovation stems from the derivation of a robust SSM based on Watershed segmentation which steers the elastic registration at some critical zones. METHODS. The basic idea is to relate only corresponding parts of the shape under investigation. A sample from the samples set is chosen as a common reference (atlas), and the other samples are landmarked and registered to it so that the corresponding points can be identified. The registration has three levels: alignment, rigid and elastic transformations. To align two scapulae, we define a coordinate system, attach it to each scapula and align both systems. For this, we automatically locate three characteristic points on the scapula's surface. All samples are then scaled to the atlas and the rigid registration is determined by minimising the Euclidian distance between surfaces using Levenberg-Marquadt algorithm. Afterwards, the samples are locally deformed toward the atlas using directly their landmarks (traditional approach). Unfortunately, landmarks-correspondences could be mismatched at some anatomically complex, “critical,” zones of the scapula. To overcome such a problem, we suggest to 3D-segment these “critical” zones using a 3D Watershed-based method. Watershed is based on a physical concept of immersion, where it is achieved in a similar way to water filling geographic basins. We believe that this is a natural way to segment the surface of the scapula since it has two large “basins”: the glenoid and the subscapularis fossa. Watershed is followed by geometrical operations to establish eight separated zones on the surface of the scapula. Once we have the zones, surface-to-surface correspondence is defined and the landmarks' point-to-point correspondences are obtained within each zone pair separately. The elastic registration is then applied on the whole surface via a multi-resolution B-Spline algorithm. The atlas is built through an iterative procedure to eliminate the bias to the initial choice and the correspondences are identified by a reverse registration. Finally, the statistical model can be constructed by performing Principle Component Analysis (PCA). RESULTS. The framework was tested on a set of 14 surfaces of cadaveric scapulae. We calculate an error metric across all the set samples by registering each sample into the remainder of the samples using the traditional and the new approach. The error is calculated using the surface-to-surface correspondence to increase the reliability of the validation. Results show that using the new approach could significantly decrease the mean error and narrow its variance. On the other hand, it gives a “cleaner” SSM than the traditional one (i.e. it can carry more representative variations for a given set of samples.)