Application of deep learning for automated diagnosis and classification of hip dysplasia on plain radiographs.

BACKGROUND: Hip dysplasia is a condition where the acetabulum is too shallow to support the femoral head and is commonly considered a risk factor for hip osteoarthritis. The objective of this study was to develop a deep learning model to diagnose hip dysplasia from plain radiographs and classify dysplastic hips based on their severity. METHODS: We collected pelvic radiographs of 571 patients from two single-center cohorts and one multicenter cohort. The radiographs were split in half to create hip radiographs (n = 1022). One orthopaedic surgeon and one resident assessed the radiographs for hip dysplasia on either side. We used the center edge (CE) angle as the primary diagnostic criteria. Hips with a CE angle < 20°, 20° to 25°, and > 25° were labeled as dysplastic, borderline, and normal, respectively. The dysplastic hips were also classified with both Crowe and Hartofilakidis classification of dysplasia. The dataset was divided into train, validation, and test subsets using 80:10:10 split-ratio that were used to train two deep learning models to classify images into normal, borderline and (1) Crowe grade 1-4 or (2) Hartofilakidis grade 1-3. A pre-trained on Imagenet VGG16 convolutional neural network (CNN) was utilized by performing layer-wise fine-turning. RESULTS: Both models struggled with distinguishing between normal and borderline hips. However, achieved high accuracy (Model 1: 92.2% and Model 2: 83.3%) in distinguishing between normal/borderline vs. dysplastic hips. The overall accuracy of Model 1 was 68% and for Model 2 73.5%. Most misclassifications for the Crowe and Hartofilakidis classifications were +/- 1 class from the correct class. CONCLUSIONS: This pilot study shows promising results that a deep learning model distinguish between normal and dysplastic hips with high accuracy. Future research and external validation are warranted regarding the ability of deep learning models to perform complex tasks such as identifying and classifying disorders using plain radiographs. LEVEL OF EVIDENCE: Diagnostic level IV.

Does bone penetration of cement differ by cement type and application time-point?

Intrusion of cement into bone is often considered an indirect indicator for implant stability in cemented joint replacement procedures. However, the influence of cement type (different viscosities/manufacturers) and application time-point on penetration of cements continues to be debated. This study aimed to quantify the effect of cement type and application time-point on the depth of penetration using porcine tibial specimens. Four different bone cements were applied to 60 resected porcine cadaveric tibias at three time-points within the working window (1, 2, and 3 min after dough time). Penetration was measured using computed tomography, utilizing two rigorous methods from the literature and a newly proposed volumetric method. Application time-point had a strong influence on the thickness of the cement layer above the resected tibia (0.25, 0.49, 0.73 mm at the three time-points). No significant variation in penetration depth metrics with cement type or application time-point was found, except percentage area covered by cement at 2 mm depth. This metric was significantly different between 1 and 3-minute time-points (12% and 6% respectively). Time-point of application had a significant influence on thickness of pure cement layer over resected bone. However, penetration depth was not significantly affected by cement type or application time-point. The clinical significance of these findings is that it may be better to apply cement relatively soon after dough time to avoid excessively thick cement mantle between implant and bone. Further, the choice of cement type may have minimal impact on cement penetration, indicating that long standing principles of good cement application maybe more important.

Synthesis and Characterization of Carbon Nanotube-Doped Thermoplastic Nanocomposites for the Additive Manufacturing of Self-Sensing Piezoresistive Materials.

We present carbon nanotube (CNT)-reinforced polypropylene random copolymer (PPR) nanocomposites for the additive manufacturing of self-sensing piezoresistive materials via fused filament fabrication. The PPR/CNT feedstock filaments were synthesized through high shear-induced melt blending with controlled CNT loading up to 8 wt % to enable three-dimensional (3D) printing of nanoengineered PPR/CNT composites. The CNTs were found to enhance crystallinity (up to 6%) in PPR-printed parts, contributing to the overall CNT-reinforcement effect that increases both stiffness and strength (increases of 56% in modulus and 40% in strength at 8 wt % CNT loading). Due to electrical conductivity (∼10-4-10-1 S/cm with CNT loading) imparted to the PPR by the CNT network, multifunctional in situ strain and damage sensing in 3D-printed CNT/PPR bulk composites and lattice structures are revealed. A useful range of gauge factors (k) is identified for strain sensing (ks = 10.1-17.4) and damage sensing (kd = 20-410) across the range of CNT loadings for the 0° print direction. Novel auxetic re-entrant and S-unit cell lattices are printed, with multifunctionality demonstrated as strain- and damage-sensing in tension. The PPR/CNT multifunctional nanocomposite lattices demonstrated here exhibit tunable strain and damage sensitivity and have application in biomedical engineering for the creation of self-sensing patient-specific devices such as orthopedic braces, where the ability to sense strain (and stress) can provide direct information for optimization of brace design/fit over the course of treatment.

Defining and predicting radiographic knee osteoarthritis progression: a systematic review of findings from the osteoarthritis initiative.

PURPOSE: The purposes of this systematic review were to (1) identify the commonly used definitions of radiographic KOA progression, (2) summarize the important associative risk factors for disease progression based on findings from the OAI study and (3) summarize findings from radiographic KOA progression prediction modeling studies regarding the characterization of progression and outcomes. METHODS: A systematic review was performed by conducting a literature search of definitions, risk factors and predictive models for radiographic KOA progression that utilized data from the OAI database. Radiographic progression was further characterized into "accelerated KOA" and "typical progression," as defined by included studies. RESULTS: Of 314 studies identified, 41 studies were included in the present review. Twenty-eight (28) studies analyzed risk factors associated with KOA progression, and 13 studies created or validated prediction models or risk calculators for progression. Kellgren-Lawrence (KL) grade based on radiographs was most commonly used to characterize KOA progression (50%), followed by joint space width (JSW) narrowing (32%) generally over 48 months. Risk factors with the highest odds ratios (OR) for progression included periarticular bone mineral density (OR 10.40), any knee injury within 1 year (OR 9.22) and baseline bone mineral lesions (OR 7.92). Nine prediction modeling studies utilized both clinical and structural risk factors to inform their models, and combined models outperformed purely clinical or structural models. CONCLUSION: The cumulative evidence suggests that combinations of structural and clinical risk factors may be able to predict radiographic KOA progression, particularly in patients with accelerated progression. Clinically relevant and feasible prediction models and risk calculators may provide valuable decision-making support when caring for patients at risk of KOA progression, although standardization in modeling and variable identification does not yet exist.

Assessing the utility of deep neural networks in predicting postoperative surgical complications: a retrospective study.

BACKGROUND: Early detection of postoperative complications, including organ failure, is pivotal in the initiation of targeted treatment strategies aimed at attenuating organ damage. In an era of increasing health-care costs and limited financial resources, identifying surgical patients at a high risk of postoperative complications and providing personalised precision medicine-based treatment strategies provides an obvious pathway for reducing patient morbidity and mortality. We aimed to leverage deep learning to create, through training on structured electronic health-care data, a multilabel deep neural network to predict surgical postoperative complications that would outperform available models in surgical risk prediction. METHODS: In this retrospective study, we used data on 58 input features, including demographics, laboratory values, and 30-day postoperative complications, from the American College of Surgeons (ACS) National Surgical Quality Improvement Program database, which collects data from 722 hospitals from around 15 countries. We queried the entire adult (≥18 years) database for patients who had surgery between Jan 1, 2012, and Dec 31, 2018. We then identified all patients who were treated at a large midwestern US academic medical centre, excluded them from the base dataset, and reserved this independent group for final model testing. We then randomly created a training set and a validation set from the remaining cases. We developed three deep neural network models with increasing numbers of input variables and so increasing levels of complexity. Output variables comprised mortality and 18 different postoperative complications. Overall morbidity was defined as any of 16 postoperative complications. Model performance was evaluated on the test set using the area under the receiver operating characteristic curve (AUC) and compared with previous metrics from the ACS-Surgical Risk Calculator (ACS-SRC). We evaluated resistance to changes in the underlying patient population on a subset of the test set, comprising only patients who had emergency surgery. Results were also compared with the Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) calculator. FINDINGS: 5 881 881 surgical patients, with 2941 unique Current Procedural Terminology codes, were included in this study, with 4 694 488 in the training set, 1 173 622 in the validation set, and 13 771 in the test set. The mean AUCs for the validation set were 0·864 (SD 0·053) for model 1, 0·871 (0·055) for model 2, and 0·882 (0·053) for model 3. The mean AUCs for the test set were 0·859 (SD 0·063) for model 1, 0·863 (0·064) for model 2, and 0·874 (0·061) for model 3. The mean AUCs of each model outperformed previously published performance metrics from the ACS-SRC, with a direct correlation between increasing model complexity and performance. Additionally, when tested on a subgroup of patients who had emergency surgery, our models outperformed previously published POTTER metrics. INTERPRETATION: We have developed unified prediction models, based on deep neural networks, for predicting surgical postoperative complications. The models were generally superior to previously published surgical risk prediction tools and appeared robust to changes in the underlying patient population. Deep learning could offer superior approaches to surgical risk prediction in clinical practice. FUNDING: The Novo Nordisk Foundation.

Patient-specific musculoskeletal models as a framework for comparing ACL function in unicompartmental versus bicruciate retaining arthroplasty.

Unicompartmental knee arthroplasty has been shown to provide superior functional outcomes compared to total knee arthroplasty and have motivated development of advanced implant designs including bicruciate retaining knee arthroplasty. However, few validated frameworks are available to directly compare the effect of implant design and surgical techniques on ligament function and joint kinematics. In the present study, the subject-specific lower extremity models were developed based on musculoskeletal modeling framework using force-dependent kinematics method, and validated against in vivo telemetric data. The experiment data of two subjects who underwent TKA were obtained from the SimTK "Grand Challenge Competition" repository, and integrated into the subject-specific lower extremity model. Five walking gait trials and three different knee implant models for each subject were used as partial inputs for the model to predict knee biomechanics for unicompartmental, bicruciate retaining, and total knee arthroplasty. The results showed no significant differences in the tibiofemoral contact forces or angular kinematic parameters between three groups. However, unicompartmental knee arthroplasty demonstrated significantly more posterior tibial location between 0% and 40% of the gait cycle (p < 0.017). Significant differences in range of tibiofemoral anterior/posterior translation and medial/lateral translation were also observed between unicompartmental and bicruciate retaining arthroplasty (p < 0.017). Peak values of anterior cruciate ligament forces differed between unicompartmental and bicruciate retaining arthroplasty from 10% to 30% of the gait cycle. Findings of this study indicate that unicompartmental and bicruciate retaining arthroplasty do not have identical biomechanics and point to the complementary role of anterior cruciate ligament and articular geometry in guiding knee function. Further, the patient-specific musculoskeletal model developed provides a reliable framework for assessing new implant designs, and effect of surgical techniques on knee biomechanics following arthroplasty.

Comparing the performance of a deep convolutional neural network with orthopedic surgeons on the identification of total hip prosthesis design from plain radiographs.

PURPOSE: A crucial step in the preoperative planning for a revision total hip replacement (THR) surgery is the accurate identification of the failed implant design, especially if one or more well-fixed/functioning components are to be retained. Manual identification of the implant design from preoperative radiographic images can be time-consuming and inaccurate, which can ultimately lead to increased operating room time, more complex surgery, and increased healthcare costs. METHOD: In this study, we present a novel approach to identifying THR femoral implants' design from plain radiographs using a convolutional neural network (CNN). We evaluated a total of 402 radiographs of nine different THR implant designs including, Accolade II (130 radiographs), Corail (89 radiographs), M/L Taper (31 radiographs), Summit (31 radiographs), Anthology (26 radiographs), Versys (26 radiographs), S-ROM (24 radiographs), Taperloc Standard Offset (24 radiographs), and Taperloc High Offset (21 radiographs). We implemented a transfer learning approach and adopted a DenseNet-201 CNN architecture by replacing the final classifier with nine fully connected neurons. Furthermore, we used saliency maps to explain the CNN decision-making process by visualizing the most important pixels in a given radiograph on the CNN's outcome. We also compared the CNN's performance with three board-certified and fellowship-trained orthopedic surgeons. RESULTS: The CNN achieved the same or higher performance than at least one of the surgeons in identifying eight of nine THR implant designs and underperformed all of the surgeons in identifying one THR implant design (Anthology). Overall, the CNN achieved a lower Cohen's kappa (0.78) than surgeon 1 (1.00), the same Cohen's kappa as surgeon 2 (0.78), and a slightly higher Cohen's kappa than surgeon 3 (0.76) in identifying all the nine THR implant designs. Furthermore, the saliency maps showed that the CNN generally focused on each implant's unique design features to make a decision. Regarding the time spent performing the implant identification, the CNN accomplished this task in ~0.06 s per radiograph. The surgeon's identification time varied based on the method they utilized. When using their personal experience to identify the THR implant design, they spent negligible time. However, the identification time increased to an average of 8.4 min (standard deviation 6.1 min) per radiograph when they used another identification method (online search, consulting with the orthopedic company representative, and using image atlas), which occurred in about 17% of cases in the test subset (40 radiographs). CONCLUSIONS: CNNs such as the one developed in this study can be used to automatically identify the design of a failed THR femoral implant preoperatively in just a fraction of a second, saving time and in some cases improving identification accuracy.

Natural language processing with deep learning for medical adverse event detection from free-text medical narratives: A case study of detecting total hip replacement dislocation.

BACKGROUND: Accurate and timely detection of medical adverse events (AEs) from free-text medical narratives can be challenging. Natural language processing (NLP) with deep learning has already shown great potential for analyzing free-text data, but its application for medical AE detection has been limited. METHOD: In this study, we developed deep learning based NLP (DL-NLP) models for efficient and accurate hip dislocation AE detection following primary total hip replacement from standard (radiology notes) and non-standard (follow-up telephone notes) free-text medical narratives. We benchmarked these proposed models with traditional machine learning based NLP (ML-NLP) models, and also assessed the accuracy of International Classification of Diseases (ICD) and Current Procedural Terminology (CPT) codes in capturing these hip dislocation AEs in a multi-center orthopaedic registry. RESULTS: All DL-NLP models outperformed all of the ML-NLP models, with a convolutional neural network (CNN) model achieving the best overall performance (Kappa = 0.97 for radiology notes, and Kappa = 1.00 for follow-up telephone notes). On the other hand, the ICD/CPT codes of the patients who sustained a hip dislocation AE were only 75.24% accurate. CONCLUSIONS: We demonstrated that a DL-NLP model can be used in largescale orthopaedic registries for accurate and efficient detection of hip dislocation AEs. The NLP model in this study was developed with data from the most frequently used electronic medical record (EMR) system in the U.S., Epic. This NLP model could potentially be implemented in other Epic-based EMR systems to improve AE detection, and consequently, quality of care and patient outcomes.

Leveraging subject-specific musculoskeletal modeling to assess effect of anterior cruciate ligament retaining total knee arthroplasty during walking gait.

Bi-cruciate retaining total knee arthroplasty has several potential advantages including improved anteroposterior knee stability compared to contemporary posterior cruciate-retaining total knee arthroplasty. However, few studies have explored whether there is significant differences of knee biomechanics following bi-cruciate retaining total knee arthroplasty compared to posterior cruciate-retaining total knee arthroplasty. In the present study, subject-specific lower extremity musculoskeletal multi-body dynamics models for bi-cruciate retaining, bi-cruciate retaining without anterior cruciate ligament, and posterior cruciate-retaining total knee arthroplasty were developed based on the musculoskeletal modeling framework using force-dependent kinematics method and validated against in vivo telemetric data. The experiment data of two subjects who underwent total knee arthroplasty were obtained for the SimTK "Grand Challenge Competition" repository, and integrated into the musculoskeletal model. Five walking gait trials for each subject were used as partial inputs for the model to predict the knee biomechanics for bi-cruciate retaining, bi-cruciate retaining without anterior cruciate ligament, and posterior cruciate-retaining total knee arthroplasty. The results revealed significantly greater range of anterior/posterior tibiofemoral translation, and significantly more posterior tibial location during the early phase of gait and more anterior tibial location during the late phase of gait were found in bi-cruciate retaining total knee arthroplasty without anterior cruciate ligament when compared to the bi-cruciate retaining total knee arthroplasty. No significant differences in tibiofemoral contact forces, rotations, translations, and ligament forces between bi-cruciate retaining and posterior cruciate-retaining total knee arthroplasty during normal walking gait, albeit slight differences in range of tibiofemoral internal/external rotation and anterior/posterior translation were observed. The present study revealed that anterior cruciate ligament retention has a positive effect on restoring normal knee kinematics in bi-cruciate retaining total knee arthroplasty. Preservation of anterior cruciate ligament in total knee arthroplasty and knee implant designs interplay each other and both contribute to restoring normal knee kinematics in different types of total knee arthroplasty. Further evaluation of more demanding activities and subject data from patients with bi-cruciate retaining and posterior cruciate-retaining total knee arthroplasty via musculoskeletal modeling may better highlight the role of the anterior cruciate ligament and its stabilizing influence.

Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network.

Identifying the design of a failed implant is a key step in the preoperative planning of revision total joint arthroplasty. Manual identification of the implant design from radiographic images is time-consuming and prone to error. Failure to identify the implant design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in the identification of three commonly used THR implant designs. Such CNN can be used to automatically identify the design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners' time, and reduce healthcare costs.

Evaluation of pull-off strength and seating displacement of sleeved ceramic revision heads in modular hip arthroplasty.

Corrosion in revision total hip arthroplasty can be mitigated using a ceramic head on a well-fixed in situ stem, but concerns of their early failure because of any surface defects on in situ stem necessitates the use of a titanium sleeve, which furnishes a factory-finish surface. These sleeves are manufactured in different sizes allowing neck-length adjustment. The strength of the taper junction of non-sleeved primary heads is well-investigated, but the influence of an interposed titanium sleeve on achieving a secure taper lock is unclear. Therefore, this study aimed to investigate the pull-off strength and seating displacement of revision ceramic heads and titanium taper sleeves. Two different head diameters and two different taper adapter sleeve offset lengths were mated with trunnions at two different impaction forces. The seating displacement and pull-off force was recorded for each specimen. Profilometry of the grooved outer surfaces of the sleeve and trunnion was done before and after testing to analyze the change in surface roughness. The influence of head diameter, sleeve offset, and impaction force on seating displacement and pull-off force was analyzed using analysis of covariance. Pull-off forces for 6 kN assembly force were approximately three times those for 2 kN. The head diameter did not have a significant effect on the measured parameters. Compared with short offset length sleeves, extra-long increased seating displacement by 31% and reduced pull-off forces by 15%. While sleeves of different offset lengths permit control of neck length, surgeons must be careful of the impact of this choice on the stability of implant. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 38:1523-1528, 2020.

Quantification of Adhesion Force of Bacteria on the Surface of Biomaterials: Techniques and Assays.

Biomaterials associated infection (BAI) has been identified as one of the leading causes of failure of bioimplants. A failed implant requires revision surgery, which is about 20 times costlier and more painful than primary surgery. Infection starts from initial attachment of bacteria onto the surface of biomaterials followed by colonization and biofilm formation. Once a biofilm is developed the bacteria become resistant toward antibiotics. On account of microbial cell development, their metabolic activity and viability are strongly affected by the adhesion. Hence a thorough investigation warrants an in-depth understanding of the interfacial adhesion. Several methods such as plate-and-wash assay, spinning-disc assay, centrifugation assay, step-pressure technique, optical tweezers, atomic force microscopy (AFM) and nanoindentation are used for the measurement of the bacterial adhesion. Most of the aforementioned techniques are nonquantitative and provide only approximate values of adhesion forces. Techniques such as AFM and nanoindentation can quantify a wide range of force 10 pN to 1 µN and 1 nN to 10 µN respectively, and hence they are particularly useful for exact quantification of the adhesion force as well as adhesion strength of bacterial cells on various surfaces of biomaterials. In this review, we present a comparative study of the techniques available to measure the bacterial adhesion force and strength, discuss the use of AFM in adhesion force quantification in detail and conclude by hypothesizing that the AFM technique has an edge over other techniques for quantification of bacterial adhesion force.

Cadaver-Specific Models for Finite-Element Analysis of Iliopsoas Impingement in Dual-Mobility Hip Implants.

BACKGROUND: Joint dislocation is a major cause of failure in total hip arthroplasty. Dual-mobility implants provide a femoral head diameter that can match the native hip size for greater stability against dislocation. However, such large heads are prone to impingement against surrounding soft tissues. To address this concern, the concept of an anatomically contoured dual-mobility implant was evaluated using cadaver-specific finite-element analysis (FEA). METHODS: The stiffness of 10 iliopsoas tendons was measured and also 3D bone models, contact pressure, and iliopsoas tendon stress were evaluated for 2 implant designs according to a previous cadaveric experiment. The iliopsoas interaction with an anatomically contoured and conventional dual-mobility implant was analyzed throughout hip flexion. RESULTS: The tensile test of cadaveric iliopsoas tendons revealed an average linear stiffness of 339.4 N/mm, which was used as an input for the FEA. Tendon-liner contact pressure and tendon von Mises stress decreased with increasing hip flexion for both implants. Average contact pressure and von Mises stresses were lower in the anatomically contoured design compared with the conventional implant across all specimens and hip flexion angles. CONCLUSIONS: This study was built upon a previous cadaver study showing reduced tenting of the iliopsoas tendon for an anatomically contoured design compared with a conventional dual-mobility implant. The present cadaver-specific FEA study found reduced tendon-liner contact pressure and tendon stresses with contoured dual-mobility liners. Anatomical contoured design may be a solution to avoid anterior soft-tissue impingement when using hip prostheses with large femoral heads.

Analysis of Dual Mobility Liner Rim Damage Using Retrieved Components and Cadaver Models.

BACKGROUND: The objective of this study was to assess the retentive rim of retrieved dual mobility liners for visible evidence of deformation from femoral neck contact and to use cadaver models to determine if anterior soft tissue impingement could contribute to such deformation. METHODS: Fifteen surgically retrieved polyethylene liners were assessed for evidence of rim deformation. The average time in vivo was 31.4 months, and all patients were revised for reasons other than intraprosthetic dislocation. Liner interaction with the iliopsoas was studied visually and with fluoroscopy in cadaver specimens using a dual mobility system different than the retrieval study. For fluoroscopic visualization, a metal wire was sutured to the iliopsoas and wires were also embedded into grooves on the outer surface of the liner and the inner head. RESULTS: All retrievals showed evidence of femoral neck contact. The cadaver experiments showed that liner motion was impeded by impingement with the iliopsoas tendon in low flexion angles. When observing the hip during maximum hyperextension, 0°, 15°, and 30° of flexion, there was noticeable tenting of the iliopsoas caused by impingement with the liner. CONCLUSION: Liner rim deformation resulting from contact with the femoral neck likely begins during early in vivo function. The presence of deformation is indicative of a mechanism inhibiting mobility of the liner. The cadaver studies showed that liner motion could be impeded because of its impingement with the iliopsoas. Such soft tissue impingement may be one mechanism by which liner motion is routinely inhibited, which can result in load transfer from the neck to the rim.

In vivo function of posterior cruciate ligament before and after posterior cruciate ligament-retaining total knee arthroplasty.

PURPOSE: The object of this study was to investigate the in vivo function of the posterior cruciate ligament (PCL) in patients before and after a PCL-retaining total knee arthroplasty (TKA). METHODS: Eleven patients with advanced osteoarthritis (OA) of the knee were recruited. Magnetic resonance scans of each OA knee were obtained, and 3D computer models, including the femoral and tibial insertion areas of the anterolateral and posteromedial bundles of the PCL, were created. Before and after PCL-retaining TKA, dual fluoroscopic images of each knee were acquired during weight-bearing knee flexion. The images and computer models were used to reproduce the in vivo motion of the knee. The function of the PCL bundles was described in terms of elongation, elevation and deviation. Twenty-two healthy controls were also included as normal references. RESULTS: PCL bundles of the OA knees were overstretched during late knee flexion and orientated more medially throughout flexion compared with normal knees. After PCL-retaining TKA, PCL bundles were further overstretched during late flexion and changed from medially directed in normal and OA knees to almost sagittally directed, which may compromise function in controlling knee rotation. CONCLUSIONS: The current PCL-retaining TKA systems and surgical techniques may not adequately re-establish normal biomechanics of PCL bundles after PCL-retaining TKA.

Do high flexion posterior stabilised total knee arthroplasty designs increase knee flexion? A meta analysis.

PURPOSE: This systematic literature review analysed the change in range of knee flexion from pre-operative values, following conventional posterior stabilised (PS) and high-flexion (H-F) PS total knee arthroplasty (TKA). METHODS: We calculated the weighted mean differences of pre- and postoperative flexion using meta-analysis with random effect modelling. Eighteen studies met our inclusion criteria. These data included a total of 2,104 PS knees that received conventional implants and 518 knees that received H-F implants. RESULTS: The pooled gain in flexion was 4.70° in the conventional group (p <0.0001) and 4.81° in the H-F group (p = 0.0008). In the subgroup analysis, the Western patient group showed significant difference in the gain of flexion with both implants. In contrast, no significant gain in flexion was observed in the Asian patient group. CONCLUSIONS: These results suggest that improvement of preoperative flexion after TKA using current H-F PS prostheses is similar to that of conventional PS prostheses.

Differences of knee anthropometry between Chinese and white men and women.

Whether there are differences in knee anthropometry between Asian and white knees remains unclear. Three-dimensional knee models were constructed using computed tomography or magnetic resonance imaging of healthy Chinese and white subjects. The morphologic measurements of the femur included mediolateral, anteroposterior dimensions, and aspect ratio. The tibial measurements included mediolateral, medial/lateral anteroposterior dimension, aspect ratio, and posterior slope of medial/lateral plateau. The results showed that Chinese knees were generally smaller than white knees. In addition, the femoral aspect ratio of Chinese females was significantly smaller than that of white females (1.24 ± 0.04 vs 1.28 ± 0.06). Tibial aspect ratio differences between Chinese and white males (1.82 ± 0.07 vs 1.75 ± 0.11), though significant, were likely a reflection of differences in knee size between races. These racial differences should be considered in the design of total knee arthroplasty prosthesis for Asian population.

Kinematics of medial osteoarthritic knees before and after posterior cruciate ligament retaining total knee arthroplasty.

Total knee arthroplasty (TKA) is a widely accepted surgical procedure for the treatment of patients with end-stage osteoarthritis (OA). However, the function of the knee is not always fully recovered after TKA. We used a dual fluoroscopic imaging system to evaluate the in vivo kinematics of the knee with medial compartment OA before and after a posterior cruciate ligament-retaining TKA (PCR-TKA) during weight-bearing knee flexion, and compared the results to those of normal knees. The OA knees displayed similar internal/external tibial rotation to normal knees. However, the OA knees had less overall posterior femoral translation relative to the tibia between 0° and 105° flexion and more varus knee rotation between 0° and 45° flexion, than in the normal knees. Additionally, in the OA knees the femur was located more medially than in the normal knees, particularly between 30° and 60° flexion. After PCR-TKA, the knee kinematics were not restored to normal. The overall internal tibial rotation and posterior femoral translation between 0° and 105° knee flexion were dramatically reduced. Additionally, PCR-TKA introduced an abnormal anterior femoral translation during early knee flexion, and the femur was located lateral to the tibia throughout weight-bearing flexion. The data help understand the biomechanical functions of the knee with medial compartment OA before and after contemporary PCR-TKA. They may also be useful for improvement of future prostheses designs and surgical techniques in treatment of knees with end-stage OA.

Are current total knee arthroplasty implants designed to restore normal trochlear groove anatomy?

Biomechanical studies have shown that external rotation of the femoral TKA component improves patellar tracking but does not restore it to physiologic values. We hypothesized that this could be due to differences in the trochlear groove geometry of TKA and normal knees. This was investigated via a virtual TKA procedure that mounted femoral components on to 3-dimensional models of healthy femurs, followed by measurement of the trochlear geometry before and after the simulated TKA. The results showed that (1) external rotation of the component brought the trochlear groove closer to normal anatomy than no external rotation; (2) however, even with external rotation, the trochlear anatomy was only partially restored to normal. Further work is needed to determine implications for patellofemoral complications observed with current TKA designs.

Gender differences in the knees of Chinese population.

The goal of this study was to characterize the geometry of the distal femur and proximal tibia in the Chinese population. Three-dimensional models of twenty female and twenty male knees were constructed using CT images. The morphologic measurements of the distal femur included mediolateral (ML) and anteroposterior dimension of medial and lateral condyles (MAP, LAP), femoral aspect ratio (ML/LAP), medial and lateral condylar width, intercondylar notch width, notch width index (NWI), and trochlear groove orientation. The sagittal profiles of the medial and lateral femoral condyles and tibial plateaus were also characterized. The results showed that the size of the distal femur of the females was significantly smaller than that of the males. Furthermore, when normalized by LAP, the females had a significantly narrower distal femur (ML), and a shorter MAP compared to the males. In the sagittal plane, the radius of the lateral distal circle of the femur was significantly smaller than that of the medial condyle in both genders. There were no significant gender differences in the proximal tibial geometry. The data of the present study may enable suitable modification of total knee prosthesis sizing/geometry for Asia-Pacific patients.