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Background: The Osteoarthritis Initiative (OAI) collected extensive imaging data, including Multi-Echo Spin-Echo (MESE) sequences for measuring knee cartilage T 2 relaxation times. Mono-exponential models are used in the OAI for T 2 fitting, which neglects stimulated echoes and B 1 inhomogeneities. Extended Phase Graph (EPG) modeling addresses these limitations but has not been applied to the OAI dataset., Purpose: To assess how different fitting methods, including EPG-based and exponential-based approaches, affect the accuracy and reproducibility of cartilage T 2 in the OAI dataset., Study Type: Retrospective., Population: From OAI dataset, 50 subjects, stratified by osteoarthritis (OA) severity using Kellgren-Lawrence grades (KLG), and 50 subjects without OA diagnosis during OAI duration were selected (each group: 25 females, mean ages ~61 years)., Field Strength/sequence: 3-T, two-dimensional (2D) MESE sequence., Assessment: Femoral and tibial cartilages were segmented from DESS images, subdivided into seven sub-regions, and co-registered to MESE. T 2 maps were obtained using three EPG-based methods (nonlinear least squares, dictionary matching, and deep learning) and three mono-exponential approaches (linear least squares, nonlinear least squares, and noise-corrected exponential). Average T 2 values within sub-regions were obtained. Pair-wise agreement among fitting methods was evaluated using the stratified subjects, while reproducibility using healthy subjects. Each method's T 2 accuracy and repeatability varying signal-to-noise ratio (SNR) were assessed with simulations., Statistical Tests: Bland-Altman analysis, Lin's concordance coefficient, and coefficient of variation assessed agreement, repeatability, and reproducibility. Statistical significance was set at P-value <0.05., Results: EPG-based methods demonstrated superior T 2 accuracy (mean absolute error below 0.5 msec at SNR > 100) compared to mono-exponential methods (error > 7 msec). EPG-based approaches had better reproducibility, with limits of agreement 1.5-5 msec narrower than exponential-based methods. T 2 values from EPG methods were systematically 10-17 msec lower than those from mono-exponential fitting., Data Conclusion: EPG modeling improved agreement and reproducibility of cartilage T 2 mapping in subjects from the OAI dataset., Evidence Level: 3 TECHNICAL EFFICACY: Stage 1., (© 2024 International Society for Magnetic Resonance in Medicine.)

Analyzing anatomic shapes of tissues and organs is pivotal for accurate disease diagnostics and clinical decision-making. One prominent disease that depends on anatomic shape analysis is osteoarthritis, which affects 30 million Americans. To advance osteoarthritis diagnostics and prognostics, we introduce ShapeMed-Knee, a 3D shape dataset with 9,376 high-resolution, medical-imaging-based 3D shapes of both femur bone and cartilage. Besides data, ShapeMed-Knee includes two benchmarks for assessing reconstruction accuracy and five clinical prediction tasks that assess the utility of learned shape representations. Leveraging ShapeMed-Knee, we develop and evaluate a novel hybrid explicit-implicit neural shape model which achieves up to 40% better reconstruction accuracy than a statistical shape model and implicit neural shape model. Our hybrid models achieve state-of-the-art performance for preserving cartilage biomarkers; they're also the first models to successfully predict localized structural features of osteoarthritis, outperforming shape models and convolutional neural networks applied to raw magnetic resonance images and segmentations. The ShapeMed-Knee dataset provides medical evaluations to reconstruct multiple anatomic surfaces and embed meaningful disease-specific information. ShapeMed-Knee reduces barriers to applying 3D modeling in medicine, and our benchmarks highlight that advancements in 3D modeling can enhance the diagnosis and risk stratification for complex diseases. The dataset, code, and benchmarks will be made freely accessible.

Purpose: Rotator cuff related shoulder pain is common among older adults. While exercise is often recommended for this condition, the relationship between physical activity levels and self-reported outcomes is unclear. This study investigated whether self-reported outcomes (pain, function, depressive symptoms, self-efficacy, general mental and physical health) relate to physical activity, and whether those who participated in shoulder-specific exercise had better self-reported outcomes than those who performed whole-body exercise., Methods: Forty-six participants with rotator cuff related shoulder pain participated from which 35 had complete datasets. Questionnaires were used to assess physical activity, pain, physical function, general health, and self-efficacy. Physical activity levels were also measured using an accelerometer., Results: Neither pain nor other self-reported outcomes were related to subjective or objective physical activity levels. Participants that completed shoulder-specific exercise had significantly higher exercise self-efficacy than those who completed nonspecific exercise ( P  = .01; d  = 0.91)., Conclusion: A significant relationship between pain or self-reported outcomes and physical activity was not identified. Those who self-reported regularly exercising their injured shoulder had higher exercise self-efficacy than those who did not. These findings have clinical implications, suggesting that strategies to boost exercise self-efficacy may be important for older adults with rotator cuff related shoulder pain.

Bicycle set-up dimensions and cycling kinematic data are important components of bicycle fitting and cyclist testing protocols. However, there are no guidelines on how bicycles should be measured and how kinematic data should be collected to increase the reliability of outcomes. This article proposes a consensus regarding bicycle set-up dimensions and recommendations for collecting cycling-related kinematic data. Four core members recruited panellists, prepared the document to review in each round for panellists, analysed the scores and comments of the expert panellists, reported the decisions and communicated with panellists. Fourteen experts with experience in research involving cycling kinematics and/or bicycle fitting agreed to participate as panellists. An initial list of 17 statements was proposed, rated using a five-point Likert scale and commented on by panellists in three rounds of anonymous surveys following a Delphi procedure. The consensus was agreed upon when more than 80% of the panellists scored the statement with values of 4 and 5 (moderately and strongly agree) with an interquartile range of less than or equal to 1. A consensus was achieved for eight statements addressing bicycle set-up dimensions (e.g. saddle height, saddle setback, etc.) and nine statements for cycling kinematic assessment (e.g. kinematic method, two-dimensional methodology, etc.). This consensus statement provides a list of recommendations about how bicycle set-up dimensions should be measured and the best practices for collecting cycling kinematic data. These recommendations should improve the transparency, reproducibility, standardisation and interpretation of bicycle measurements and cycling kinematic data for researchers, bicycle fitters and cycling related practitioners., (© 2024. The Author(s).)

Introduction: Endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) for lung cancer staging is operator dependent, resulting in high rates of non-diagnostic lymph node (LN) samples. We hypothesized that an artificial intelligence (AI) algorithm can consistently and reliably predict nodal metastases from the ultrasound images of LNs when compared to pathology., Methods: In this analysis of prospectively recorded B-mode images of mediastinal LNs during EBUS-TBNA, we used transfer learning to build an end-to-end ensemble of three deep neural networks (ResNet152V2, InceptionV3, and DenseNet201). Model hyperparameters were tuned, and the optimal version(s) of each model was trained using 80% of the images. A learned ensemble (multi-layer perceptron) of the optimal versions was applied to the remaining 20% of the images (Test Set). All predictions were compared to the final pathology from nodal biopsies and/or surgical specimen., Results: A total of 2,569 LN images from 773 patients were used. The Training Set included 2,048 LNs, of which 70.02% were benign and 29.98% were malignant on pathology. The Testing Set included 521 LNs, of which 70.06% were benign and 29.94% were malignant on pathology. The final ensemble model had an overall accuracy of 80.63% (95% confidence interval [CI]: 76.93-83.97%), 43.23% sensitivity (95% CI: 35.30-51.41%), 96.91% specificity (95% CI: 94.54-98.45%), 85.90% positive predictive value (95% CI: 76.81-91.80%), 79.68% negative predictive value (95% CI: 77.34-81.83%), and AUC of 0.701 (95% CI: 0.646-0.755) for malignancy., Conclusion: There now exists an AI algorithm which can identify nodal metastases based only on ultrasound images with good overall accuracy, specificity, and positive predictive value. Further optimization with larger sample sizes would be beneficial prior to clinical application., (© 2024 S. Karger AG, Basel.)

: To develop and assess an automatic and robust knee musculoskeletal finite element (MSK-FE) modeling pipeline., Methods: Magnetic resonance images (MRIs) were used to train nnU-Net networks for auto-segmentation of knee bones (femur, tibia, patella, and fibula), cartilages (femur, tibia, and patella), menisci, and major knee ligaments. Two different MRI sequences were used to broaden applicability. Next, we created MSK-FE models of an unseen dataset using two MSK-FE modeling pipelines: template-based and auto-meshing. MSK models had personalized knee geometries with multi-degree-of-freedom elastic foundation contacts. FE models used fibril-reinforced poroviscoelastic swelling material models for cartilages and menisci., Results: Volumes of knee bones, cartilages, and menisci did not significantly differ (p>0.05) across MRI sequences. MSK models estimated secondary knee kinematics during passive knee flexion tests consistent with in vivo and simulation-based values from the literature. Between the template-based and auto-meshing FE models, estimated cartilage mechanics often differed significantly (p<0.05), though differences were <15% (considering peaks during walking), i.e., <1.5 MPa for maximum principal stress, <1 percentage point for collagen fibril strain, and <3 percentage points for maximum shear strain., Conclusion: The template-based modeling provided a more rapid and robust tool than the auto-meshing approach, while the estimated knee biomechanics were comparable. Nonetheless, the auto-meshing approach might provide more accurate estimates in subjects with distinct knee irregularities, e.g., cartilage lesions., Significance: The MSK-FE modeling tool provides a rapid, easy-to-use, and robust approach for investigating task- and person-specific mechanical responses of the knee cartilage and menisci, holding significant promise, e.g., in personalized rehabilitation planning.

Objective: Endobronchial ultrasound elastography produces a color map of mediastinal lymph nodes, with the color blue (level 60) indicating stiffness. Our pilot study demonstrated that predominantly blue lymph nodes, with a stiffness area ratio greater than 0.496, are likely malignant. This large-scale study aims to validate this stiffness area ratio compared with pathology., Methods: This is a single-center prospective clinical trial where B-mode ultrasound and endobronchial ultrasound elastography lymph node images were collected from patients undergoing endobronchial ultrasound transbronchial needle aspiration for suspected or diagnosed non-small cell lung cancer. Images were fed to a trained deep neural network algorithm (NeuralSeg), which segmented the lymph nodes, identified the percent of lymph node area above the color blue threshold of level 60, and assigned a malignant label to lymph nodes with a stiffness area ratio above 0.496. Diagnostic statistics and receiver operating characteristic analyses were conducted. NeuralSeg predictions were compared with pathology., Results: B-mode ultrasound and endobronchial ultrasound elastography lymph node images (n = 210) were collected from 124 enrolled patients. Only lymph nodes with conclusive pathology results (n = 187) were analyzed. NeuralSeg was able to predict 98 of 143 true negatives and 34 of 44 true positives, resulting in an overall accuracy of 70.59% (95% CI, 63.50-77.01), sensitivity of 43.04% (95% CI, 31.94-54.67), specificity of 90.74% (95% CI, 83.63-95.47), positive predictive value of 77.27% (95% CI, 64.13-86.60), negative predictive value of 68.53% (95% CI, 64.05-72.70), and area under the curve of 0.820 (95% CI, 0.758-0.883)., Conclusions: NeuralSeg was able to predict nodal malignancy based on endobronchial ultrasound elastography lymph node images with high area under the receiver operating characteristic curve and specificity. This technology should be refined further by testing its validity and applicability through a larger dataset in a multicenter trial., Competing Interests: A.A.G.: ACLIP LLC (Owner/Co-Owner Founder/Co-Founder, Ownership Interest (stocks, stock options, patent or other intellectual property or other ownership interest excluding diversified mutual funds)), NeuralSeg Ltd (Owner/Co-Owner Founder/Co-Founder). W.C.H.: Hamilton Academic Health Sciences Organization AFP Innovation Grant, Advisory Board and Speakers Bureau AstraZeneca, Data Safety Monitoring Committee for Roche/Genentech, Speakers Bureau for Minogue Medical, and Grant Funding from Intuitive Surgical. All other authors reported no conflicts of interest. The Journal policy requires editors and reviewers to disclose conflicts of interest and to decline handling or reviewing manuscripts for which they may have a conflict of interest. The editors and reviewers of this article have no conflicts of interest., (© 2024 The Author(s).)

The purpose was to investigate relationships of cumulative load and cartilage turnover biomarkers with 2-year changes in cartilage in knee osteoarthritis. From participants with Kellgren-Lawrence (KL) grades of 1 to 3, cartilage thickness and transverse relaxation time (T2) were computed from 24-month (baseline) and 48-month magnetic resonance images. Cumulative load was the interaction term of the Physical Activity Scale for the Elderly (PASE) and body mass index (BMI). Serum cartilage oligomeric matrix protein (COMP) and the nitrated form of type II collagen (Coll2-1 NO2) were collected at baseline. Multiple regressions (adjusted for baseline age, KL grade, cartilage measures, pain, comorbidity) evaluated the relationships of cumulative load and biomarkers with 2-year changes. In 406 participants (63.7 (8.7) years), interactions of biomarkers with cumulative load weakly predicted 2-year cartilage changes: (i) COMP × cumulative load explained medial tibia thickness change (R 2 increased 0.062 to 0.087, p < 0.001); (ii) Coll2-1 NO2 × cumulative load explained central medial femoral T2 change (R 2 increased 0.177 to 0.210, p < 0.001); and (iii) Coll2-1 NO2 × cumulative load explained lateral tibia T2 change (R 2 increased 0.166 to 0.188, p < 0.001). Moderate COMP or Coll2-1 NO2 at baseline appeared protective. High COMP or Coll2-1 NO2, particularly with high BMI and low PASE, associated with worsening cartilage. Moderate serum concentrations of cartilage turnover biomarkers, at high and low physical activity, associated with maintained cartilage outcomes over 2 years. In conclusion, high concentrations of cartilage turnover biomarkers, particularly with high BMI and low physical activity, associated with knee cartilage thinning and increasing T2 over 2 years. Key Points • Higher quality cartilage may be better able to tolerate a larger cumulative load than poor quality cartilage. • Among participants enrolled in the Osteoarthritis Initiative Biomarkers Consortium Project, a representation of cumulative load exposure and its interaction with cartilage turnover biomarkers were weakly related with 2-year change in knee cartilage. • These findings suggest that cartilage turnover is a factor that modifies the relationship between loading exposure and cartilage loss in knee OA., (© 2024. The Author(s), under exclusive licence to International League of Associations for Rheumatology (ILAR).)

Introduction: Fatty infiltration (FI) of the rotator cuff has important clinical implications. Quantitatively estimating FI using ultrasound (US) has considerable benefits for assessing FI in a non-invasive, accessible manner. This research investigated whether FI of the supraspinatus (SS) and infraspinatus (IS), estimated using US was related to intramuscular fat fractions measured from magnetic resonance images (MRI)., Methods: Data from 12 healthy young adult participants were used for analysis. US images of the SS and IS were captured using multiple transducer placement techniques from which echogenicity of the muscle region was quantified. Shoulder MRI were captured from which SS and IS were manually segmented and intramuscular fat fractions calculated. Six upper limb strength exertions were performed, resisted by a hand dynamometer., Results: IS and SS echogenicity explained a significant amount of variance in MRI fat fractions for certain body positions and transducer techniques. Echogenicity agreement was higher for IS than SS. Significant relationships were identified between strength exertions and both echogenicity and MRI muscle volume, but not MRI fat fraction., Conclusions: This research provides preliminary evidence showing that quantitative-based US methods can be used to estimate MRI calculated fat fractions for the rotator cuff., (© 2024 The Authors. Journal of Clinical Ultrasound published by Wiley Periodicals LLC.)

Background: While ultrasound (US) measures of the subacromial space (SAS) have demonstrated excellent reliability, measurements are typically captured by experts with extensive ultrasound experience. Further, the agreement between US measured SAS width and other imaging modalities has not been explored. This research evaluated the agreement between SAS measures captured by novice and expert raters and between US and magnetic resonance imaging (MRI). This study also evaluated the effect of US transducer tilt on measured SAS., Methods: Nine men and nine women participated in this study. US images were captured by a novice and expert with the participant in both seated and supine positions. An inclinometer was fixed to the US probe to measure transducer tilt. SAS width was measured in real time from freeze framed images. MRI images were captured, and the humerus and acromion manually segmented. The SAS width was measured using a custom algorithm., Results: Intraclass correlation coefficients (ICCs) between novice and expert raters were 0.74 and 0.63 for seated and supine positions, respectively. Intra-rater agreement was high for both novice (ICC = 0.83-0.84) and expert (ICC ≥ 0.94) raters. Agreement between US and MRI was poor (ICC = 0.21-0.49) but linearly related., Conclusions: Moderate agreement between novice and expert raters was demonstrated, while the agreement between US and MRI was poor. High intra-rater reliability within each rater suggests that US measures of the SAS may be completed by a novice with introductory training., (© 2023. The Author(s).)

Muscle-driven simulations have provided valuable insights in studies of walking and running, but a set of freely available simulations and corresponding experimental data for cycling do not exist. The aim of this work was to develop a set of muscle-driven simulations of cycling and to validate them by comparison with experimental data. We used direct collocation to generate simulations of 16 participants cycling over a range of powers (40-216 W) and cadences (75-99 RPM) using two optimization objectives: a baseline objective that minimized muscle effort and a second objective that additionally minimized tibiofemoral joint forces. We tested the accuracy of the simulations by comparing the timing of active muscle forces in our baseline simulation to timing in experimental electromyography data. Adding a term in the objective function to minimize tibiofemoral forces preserved cycling power and kinematics, improved similarity between active muscle force timing and experimental electromyography, and decreased tibiofemoral joint reaction forces, which better matched previously reported in vivo measurements. The musculoskeletal models, muscle-driven simulations, simulation software, and experimental data are freely shared at https://simtk.org/projects/cycling&#95;sim for others to reproduce these results and build upon this research., (© 2023. The Author(s).)

Imaging of the joint in response to loading stress may provide additional measures of joint structure and function beyond conventional, static imaging studies. Exercise such as running, stair climbing, and squatting allows evaluation of the joint response to larger loading forces than during weight bearing. Quantitative MRI (qMRI) may assess properties of cartilage and meniscus hydration and organization in vivo that have been investigated to assess the functional response of these tissues to physiological stress. [ 18 F]sodium fluoride ([ 18 F]NaF) interrogates areas of newly mineralizing bone and provides an opportunity to study bone physiology, including perfusion and mineralization rate, as a measure of joint loading stress. In this review article, methods utilizing quantitative MRI, PET, and hybrid PET-MRI systems for assessment of the joint response to loading from exercise in vivo are examined. Both methodology and results of various studies performed are outlined and discussed. Lastly, the technical considerations, challenges, and future opportunities for these approaches are addressed., (© 2023. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

Anterior cruciate ligament (ACL) injury and ACL reconstruction (ACLR) surgery are common. Laboratory-based biomechanical assessment can evaluate ACL injury risk and rehabilitation progress after ACLR; however, lab-based measurements are expensive and inaccessible to most people. Portable sensors such as wearables and cameras can be deployed during sporting activities, in clinics, and in patient homes. Although many portable sensing approaches have demonstrated promising results during various assessments related to ACL injury, they have not yet been widely adopted as tools for out-of-lab assessment. The purpose of this review is to summarize research on out-of-lab portable sensing applied to ACL and ACLR and offer our perspectives on new opportunities for future research and development. We identified 49 original research articles on out-of-lab ACL-related assessment; the most common sensing modalities were inertial measurement units, depth cameras, and RGB cameras. The studies combined portable sensors with direct feature extraction, physics-based modeling, or machine learning to estimate a range of biomechanical parameters (e.g., knee kinematics and kinetics) during jump-landing tasks, cutting, squats, and gait. Many of the reviewed studies depict proof-of-concept methods for potential future clinical applications including ACL injury risk screening, injury prevention training, and rehabilitation assessment. By synthesizing these results, we describe important opportunities that exist for clinical validation of existing approaches, using sophisticated modeling techniques, standardization of data collection, and creation of large benchmark datasets. If successful, these advances will enable widespread use of portable-sensing approaches to identify ACL injury risk factors, mitigate high-risk movements prior to injury, and optimize rehabilitation paradigms., (© 2023. The Author(s).)

Purpose: This cross-sectional study determines the sensitivity of muscle architecture and fat measurements of the rectus femoris (RF) and vastus lateralis (VL) muscles from ultrasound images acquired with varying transducer tilt, using a novel transducer attachment, in healthy adults. Secondary objectives were to estimate intrarater and interrater reliability of image measurement and acquisition, respectively. Methods: Thirty healthy adults participated (15 women and 15 men; 25 [SD 2.5] y). Ultrasound image acquisition was conducted by two raters at different transducer tilts relative to the skin: estimated perpendicular, and five measured angles (80°, 85°, 90°, 95°, 100°) using the transducer attachment. Muscle thickness (MT), subcutaneous fat thickness (FT), pennation angle (PA), and fascicle length (FL) were measured. Sensitivity and reliability were assessed using intra-class correlation coefficients (ICCs) and standard error of measurements (SEMs). Results: MT and FT for RF and VL were not sensitive to transducer tilt. However, PA and FL were sensitive to transducer tilt. MT and FT for both muscles showed high ICCs and low SEMs for intrarater and interrater reliability. For PA of both muscles, standardizing transducer tilt improved interrater ICCs and lowered SEMs. Conclusion: MT and FT measurements of RF and VL acquired at 60° knee flexion are robust to varying transducer tilt angles. PA measurements benefit from standardizing transducer tilt., (© Canadian Physiotherapy Association.)

Objective: This proof-of-principle study integrates joint reaction forces (JRFs) and bone shape to assess acute cartilage changes from walking and cycling., Methods: Sixteen women with symptomatic knee osteoarthritis were recruited. Biomechanical assessment estimated JRFs during walking and cycling. Subsamples had magnetic resonance imaging (MRI) performed before and after a 25-min walking (n = 7) and/or cycling (n = 9) activity. MRI scans were obtained to assess cartilage shape and composition (T 2 relaxation time). Bone shape was quantified using a statistical shape model built from 13 local participants and 100 MRI scans from the Osteoarthritis Initiative. Statistical parametric mapping quantified cartilage change and correlations between cartilage change with JRFs and statistical shape model features., Results: Cartilage thickness (interior lateral, Δ - 0.10 mm) and T 2 (medial, Δ - 4 ms) decreased on the tibial plateau. On the femur, T 2 change depended on the activity. Greater tibiofemoral JRF was associated with more cartilage deformation on the lateral femoral trochlea after walking (r - 0.56). Knees more consistent with osteoarthritis showed smaller decreases in tibial cartilage thickness., Discussion: Walking and cycling caused distinct patterns of cartilage deformation, which depended on knee JRFs and bone morphology. For the first time, these results show that cartilage deformation is dependent on bone shapes and JRFs in vivo., (© 2022. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).)

Background: Endobronchial ultrasound (EBUS) has features that allow a high accuracy for predicting lymph node (LN) malignancy. However their clinical application remains limited because of high operator dependency. We hypothesized that an artificial intelligence algorithm (NeuralSeg; NeuralSeg Ltd, Hamilton, Ontario, Canada) is capable of accurately identifying and predicting LN malignancy based on EBUS images., Methods: In the derivation phase EBUS images were segmented twice by an endosonographer and used as controls in 5-fold cross-validation training of NeuralSeg. In the validation phase the algorithm was tested on new images it had not seen before. Logistic regression and receiver operator characteristic curves were used to determine NeuralSeg's capability of discrimination between benign and malignant LNs, using pathologic specimens as the gold standard., Results: Two hundred ninety-eight LNs from 140 patients were used for derivation and 108 LNs from 47 patients for validation. In the derivation cohort NeuralSeg was able to predict malignant LNs with an accuracy of 73.8% (95% confidence interval [CI], 68.4%-78.7%). In the validation cohort NeuralSeg had an accuracy of 72.9% (95% CI, 63.5%-81.0%), specificity of 90.8% (95% CI, 81.9%-96.2%), and negative predictive value of 75.9% (95% CI, 71.5%-79.9%). NeuralSeg showed higher diagnostic discrimination during validation compared with derivation (c-statistic = 0.75 [95% CI, 0.65-0.85] vs 0.63 [95% CI, 0.54-0.72], respectively)., Conclusions: NeuralSeg is able to accurately rule out nodal metastasis and can possibly be used as an adjunct to EBUS when nodal biopsy is not possible or inconclusive. Future work to evaluate the algorithm in a clinical trial is required., (Copyright © 2022 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

Overuse knee injuries are common in bicycling and are often attributed to poor bicycle-fit. Bicycle-fit for knee health focuses on setting saddle height to elicit a minimum knee flexion angle of 25-40°. Equations to predict saddle height include a single input, resulting in a likely suboptimal bicycle-fit. The purpose of this work was to develop an equation to predict saddle height from anthropometrics, bicycle geometry, and user-defined joint kinematics. Methods: Forty healthy adults (17 women, 23 men; mean (SD): 28.6 (7.2) years; 24.2 (2.6) kg/m 2 ) participated. Kinematic analyses were conducted for 18 three-minute bicycling bouts including all combinations of 3 horizontal and 3 vertical saddle positions, and 2 crank arm lengths. For both minimum and maximum knee flexion, predictors were identified using Least Absolute Shrinkage and Selection Operator (LASSO) regression, and final models were fit using linear regression. Secondary analyses determined if saddle height equations were sex dependent. Results: The equation to predict saddle position from minimum knee flexion angle (R 2 =0.97; root mean squared error (RMSE) = 1.15 cm) was: Saddle height (cm) = 7.41 + 0.82(inseam cm) - 0.1(minimum knee flexion °) + 0.003(inseam cm)(seat tube angle °). The maximum knee flexion equation (R 2 =0.97; RMSE=1.15 cm) was: Saddle height (cm) = 41.63 + 0.78(inseam cm) - 0.25(maximum knee flexion °) + 0.002(inseam cm)(seat tube angle °). The saddle height equations were not dependent on sex. Conclusions: These equations provide a novel, practical strategy for bicycle-fit that accounts for rider anthropometrics, bicycle geometry and user-defined kinematics. Highlights This work developed simple equations to prescribed bicycle saddle height that elicits desired knee kinematics.Separate equations are presented for prescribing minimum or maximum knee flexion angle.Equations can be generalized to riders of both sexes, and a breadth of anthropometrics and ages.

Objectives: Accurate and efficient knee cartilage and bone segmentation are necessary for basic science, clinical trial, and clinical applications. This work tested a multi-stage convolutional neural network framework for MRI image segmentation., Materials and Methods: Stage 1 of the framework coarsely segments images outputting probabilities of each voxel belonging to the classes of interest: 4 cartilage tissues, 3 bones, 1 background. Stage 2 segments overlapping sub-volumes that include Stage 1 probability maps concatenated to raw image data. Using six fold cross-validation, this framework was tested on two datasets comprising 176 images [88 individuals in the Osteoarthritis Initiative (OAI)] and 60 images (15 healthy young men), respectively., Results: On the OAI segmentation dataset, the framework produces cartilage segmentation accuracies (Dice similarity coefficient) of 0.907 (femoral), 0.876 (medial tibial), 0.913 (lateral tibial), and 0.840 (patellar). Healthy cartilage accuracies are excellent (femoral = 0.938, medial tibial = 0.911, lateral tibial = 0.930, patellar = 0.955). Average surface distances are less than in-plane resolution. Segmentations take 91 ± 11 s per knee., Discussion: The framework learns to automatically segment knee cartilage tissues and bones from MR images acquired with two sequences, producing efficient, accurate quantifications at varying disease severities., (© 2021. European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).)

Objective: The relationship between in vivo knee load predictions and longitudinal cartilage changes has not been investigated. We undertook this study to develop an equation to predict the medial tibiofemoral contact force (MCF) peak during walking in persons with instrumented knee implants, and to apply this equation to determine the relationship between the predicted MCF peak and cartilage loss in patients with knee osteoarthritis (OA)., Methods: In adults with knee OA (39 women, 8 men; mean ± SD age 61.1 ± 6.8 years), baseline biomechanical gait analyses were performed, and annualized change in medial tibial cartilage volume (mm 3 /year) over 2.5 years was determined using magnetic resonance imaging. In a separate sample of patients with force-measuring tibial prostheses (3 women, 6 men; mean ± SD age 70.3 ± 5.2 years), gait data plus in vivo knee loads were used to develop an equation to predict the MCF peak using machine learning. This equation was then applied to the knee OA group, and the relationship between the predicted MCF peak and annualized cartilage volume change was determined., Results: The MCF peak was best predicted using gait speed, the knee adduction moment peak, and the vertical knee reaction force peak (root mean square error 132.88N; R 2 = 0.81, P < 0.001). In participants with knee OA, the predicted MCF peak was related to cartilage volume change (R 2 = 0.35, β = -0.119, P < 0.001)., Conclusion: Machine learning was used to develop a novel equation for predicting the MCF peak from external biomechanical parameters. The predicted MCF peak was positively related to medial tibial cartilage volume loss in patients with knee OA., (© 2021 The Authors. Arthritis & Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.)

Objectives: When measuring changes in knee cartilage thickness in vivo after loading, mean values may not reflect local changes. The objectives of this investigation were: (1) use statistical parametric mapping (SPM) to determine regional deformation patterns of tibiofemoral cartilage in response to running; (2) quantify regional differences in cartilage thickness between males and females; and (3) explore the influence of sex on deformation., Materials and Methods: Asymptomatic males (n = 15) and females (n = 15) had MRI imaging of their right knee before and after 15 min of treadmill running. Medial and lateral tibial, and medial and lateral weight-bearing femoral cartilage were segmented. SPM was completed on cartilage thickness maps to test the main effects of Running and Sex, and their interaction. F-statistic maps were thresholded; clusters above this threshold indicated significant differences., Results: Deformation was observed in all four compartments; the lateral tibia had the largest area of deformation (p < 0.0001). Thickness differences between sexes were observed in all four compartments, showing females have thinner cartilage (p ≤ 0.009). The lateral tibia had small clusters indicating an interaction of sex on deformation (p ≤ 0.012)., Discussion: SPM identified detailed spatial information on tibiofemoral cartilage thickness differences observed after running, and between sexes and their interaction., (© 2021. European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).)

Background: It is unknown whether a greater accumulation of knee load over a typical day is related to how cartilage responds to an acute bout of loading. This information may clarify the role of habitual activity on cartilage function., Research Question: Is there a relationship between change in tibial and femoral cartilage thickness, volume, and T2 relaxation time following running with daily cumulative knee load in women? Secondarily, is there a relationship between cartilage change following running and the statistical interaction of body mass index (BMI) and daily steps?, Methods: Participants (n = 15) completed gait analyses and wore an accelerometer over a week. Daily cumulative knee load was the statistical interaction between tibial compressive joint reaction force (JRF) impulse with the average number of daily steps measured using accelerometry. Magnetic resonance imaging scans were acquired before and immediately after 15-min of treadmill running. Changes in tibial and femoral cartilage thickness, volume, and T2 relaxation time were calculated. Multiple linear regressions tested the associations of cartilage change outcomes with: baseline (thickness, volume, T2), JRF impulse, steps, and the interaction JRF impulse*steps. Secondarily, BMI was substituted for JRF impulse., Results and Significance: Tibial volume change was explained by baseline volume, JRF impulse, steps, and JRF impulse*steps (R 2 = 0.50, p = 0.013). Additionally, tibial volume change was explained by baseline volume, BMI, steps, and BMI*steps (R 2 = 0.43, p = 0.002). Those who were more physically active with lower JRF impulse (or lower BMI) showed less change in tibial cartilage after a running exposure. This may suggest cartilage conditioning., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Objectives: To assess the effect of ankle, knee, and hip kinematics on patellofemoral and tibiofemoral joint reaction forces (JRF) during bicycling. Secondarily, to assess if sex, horizontal saddle position, or crank arm length were related to JRFs, after accounting for kinematics., Design: Experimental cross-sectional study., Methods: Forty healthy adults (mean (SD); 28.6 (7.2) years, 24.2 (2.6)kg/m 2 , 17 women) bicycled under 18 bicycling positions. One position used commercial guidelines and 17 randomly deviated from commercial. Resultant patellofemoral as well as compressive and shear tibiofemoral JRFs were calculated. Linear mixed-effects models with a random intercept of leg-nested-in-participant were used., Results: Patellofemoral resultant forces were most sensitive to all joint kinematics (i.e., sensitivity was defined as the slope of single predictor models); all JRFs were least sensitive to minimum knee flexion. Tibiofemoral compression was predicted by minimum hip flexion and maximum ankle dorsiflexion (R 2 =0.90). Tibiofemoral shear (R 2 =0.86) and the resultant patellofemoral JRF (R 2 =0.90) were predicted by minimum hip flexion, maximum ankle dorsiflexion, minimum knee flexion, and the interaction between minimum hip flexion and minimum knee flexion. Adding sex as a factor improved fit of all models. This sex-effect was driven by differences in cycling intensity, reflected by the tangential crank arm force. Horizontal saddle position and crank arm length were not related to JRFs., Conclusions: Optimizing joint kinematics should be the primary goal of bicycle-fit. JRFs were least sensitive to the current gold standard for assessing bicycle-fit, minimum knee flexion. Bicycle-fit is of particular importance for those working at high intensities., (Crown Copyright © 2020. Published by Elsevier Ltd. All rights reserved.)

Purpose: This article identifies how to assess multiple sources of measurement error and identify optimal measurement strategies for obtaining clinical outcomes. Method: Obtaining, interpreting, and using information gained from measurements is instrumental in physiotherapy. To be useful, measurements must have a sufficiently small measurement error. Traditional expressions of reliability include relative reliability in the form of an intra-class correlation coefficient and absolute reliability in the form of the standard error of measurement. Traditional metrics are limited to assessing one source of error; however, real-world measurements consist of many sources of error. The measurement framework generalizability theory (GT) allows researchers to partition measurement errors into multiple sources. GT further allows them to calculate the relative and absolute reliability of any measurement strategy, thereby allowing them to identify the optimal strategy. We provide a brief comparison of classical test theory and GT, followed by an overview of the terminology and methodology used in GT, and then an example showing how GT can be used to minimize error associated with measuring knee extension power. Conclusion: The methodology described provides tools for researchers and clinicians that enable detailed interpretation and understanding of the error associated with their measurements., (© Canadian Physiotherapy Association.)

Objective: Pain is a consistently reported barrier to physical activity by persons with knee osteoarthritis (OA). Nonetheless, few studies of knee OA have investigated the association of pain with daily walking levels. The current study assessed the relationship of 2 distinct measures of knee pain with objectively measured physical activity in adults with knee OA., Methods: This was a longitudinal, observational investigation of 59 individuals (48 women; mean ± SD age 61.1 ± 6.4 years, mean ± SD body mass index 28.1 ± 5.6 kg/m 2 ) with clinical knee OA. Data were collected every 3 months for up to 3 years. Physical activity was characterized as the average steps per day taken over at least 3 days, mea-sured by accelerometry. Pain was measured using 2 patient-administered questionnaires: the pain subscale of the Knee Injury and Osteoarthritis Outcome Score (KOOS-pain) and the P4 pain scale (P4-pain). Mixed-effects models determined the association between pain and physical activity levels (over covariates) among adults with knee OA (α = 0.05)., Results: All covariates (age [β = -3.65, P < 0.001], body mass index [β = -3.06, P < 0.001], season [spring/fall β = -6.91, P = 0.002; winter β = -14.92, P < 0.001]) were predictors of physical activity. Neither the inverted KOOS-pain (β = 0.04, P = 0.717) nor P4-pain (β = -0.37, P = 0.264) was associated with physical activity., Conclusion: Knee pain is not associated with daily walking levels in persons with mild-to-moderate, symptomatic knee OA. While pain management remains an important target of interventions, strategies to increase steps per day in this population should focus on overcoming potentially more crucial barriers to activity participation., (© 2019, American College of Rheumatology.)

Cumulative load reflects the total accumulated load across a loading exposure. Estimated cumulative load can identify individuals with or at risk for pathology. However, there is no research into the accuracy of the estimated cumulative load. This study determined: (1) which impulses, from a 500 revolution bicycling activity, accurately estimate cumulative pedal reaction force; and (2) how many impulses are required to accurately estimate cumulative pedal reaction force over 500 revolutions. Twenty-four healthy adults (mean 23.4 [SD 3.1] years; 11 men) participated. Participants performed three bicycling bouts of 10-min in duration and were randomized to one of two groups (group 1 = self-selected power and prescribed cadence of 80 revolutions per minute; group 2 = prescribed power of 100 W and self-selected cadence). The first 10 revolutions (2%) of the normal pedal reaction force (PRF N ) and resultant pedal reaction force (PRF R ), and the first five revolutions (1%) of the anterior-posterior reaction force (PRF AP ) over-estimated cumulative load. The PRF N , PRF AP , and PRF R required 80 revolutions (16%), 320 revolutions (64%) and 65 revolutions (13%), respectively, to accurately estimate cumulative load across 500 cycles. These findings highlight that the context and amount of data collected are important in producing accurate estimates of cumulative load.

Objective: Running is an easy way of meeting physical activity recommendations for individuals with knee osteoarthritis (KOA); however, it remains unknown how their cartilage reacts to running. The objective of this pilot study was to compare the effects of 30 min of running on T2 and T1ρ relaxation times of tibiofemoral cartilage in female runners with and without KOA., Methods: Ten female runners with symptomatic KOA (mean age 52.6 ± 7.6 years) and 10 without KOA (mean age 52.5 ± 7.8 years) ran for 30 min on a treadmill. Tibiofemoral cartilage T2 and T1ρ relaxation times were measured using magnetic resonance imaging prior to and immediately after the bout of running. Repeated-measures analyses of covariance (ANCOVA) were conducted to examine between-group differences across scanning times., Results: No Group × Time interactions were found for T2 (P ≥ 0.076) or T1ρ (P ≥ 0.288) relaxation times. However, runners with KOA showed increased T2 values compared with pre-running in the medial and lateral femur 55 min post-running (5.4 to 5.5%, P < 0.022) and in all four tibiofemoral compartments 90 min post-running (6.9 to 11.1%, P < 0.01). A significant group effect was found for T1ρ in the medial femur, with greater values in those with KOA compared with controls., Conclusion: While Group × Time interactions in T2 and T1ρ relaxation times remained statistically insignificant, the observed significant increases in T2 in runners with tibiofemoral osteoarthritis TFOA may suggest slower and continuing changes in the cartilage and thus a need for longer recovery after running. Future research should investigate the effects of repeated exposure to running., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Purpose: Hand-held dynamometry (HHD) can be used to evaluate strength when gold-standard isokinetic dynamometry (IKD) is not feasible. HHD is useful for measuring lower limb strength in a healthy population; however, its reliability and validity in individuals with knee osteoarthritis (OA) has received little attention. In this research, we examined the test-retest reliability and validity of HHD in older women with knee OA. We also examined the associations between reliability and symptom and disease severity. Method: A total of 28 older women with knee OA completed knee extension and flexion exertions measured using HHD and IKD. Intra-class correlation coefficients (ICC 2,3 ), standard error of measurement, and minimal detectable change were calculated. Correlation coefficients and regressions evaluated the relationships between inter-trial differences and symptom and disease severity. Results: High test-retest reliability was demonstrated for both exertions with each device (ICC 2,3 = 0.83-0.96). Variance between trials was not correlated with OA symptoms. Criterion validity was good (ICC 2,3 = 0.76), but extension yielded lower agreement than flexion. Regression analysis demonstrated that true strength can be predicted from HHD measurements. Conclusions: HHD is a reliable tool for capturing knee extension and flexion in individuals with OA. Because of lower agreement, HHD might be best suited for evaluating within-subject strength changes rather than true strength scores. However, gold-standard extension strength magnitudes may reasonably be predicted from regression equations ( r 2  = 0.82)., (© Canadian Physiotherapy Association.)

This study examined the extent to which baseline measures of quadriceps strength, quadriceps power, knee pain and self-efficacy for functional tasks, and their interactions, predicted 2-year changes in mobility performance (walking, stair ascent, stair descent) in women with knee osteoarthritis. We hypothesized that lesser strength, power and self-efficacy, and higher pain at baseline would each be independently associated with reduced mobility over 2 years, and each of pain and self-efficacy would interact with strength and power in predicting 2-year change in stair-climbing performance. This was a longitudinal, observational study of women with clinical knee osteoarthritis. At baseline and follow-up, mobility was assessed with the Six-Minute Walk Test, and stair ascent and descent tasks. Quadriceps strength and power, knee pain, and self-efficacy for functional tasks were also collected at baseline. Multiple linear regression examined the extent to which 2-year changes in mobility performances were predicted by baseline strength, power, pain, and self-efficacy, after adjusting for covariates. Data were analyzed for 37 women with knee osteoarthritis over 2 years. Lower baseline self-efficacy predicted decreased walking (β = 1.783; p = 0.030) and stair ascent (β = -0.054; p < 0.001) performances over 2 years. Higher baseline pain intensity/frequency predicted decreased walking performance (β = 1.526; p = 0.002). Lower quadriceps strength (β = 0.051; p = 0.015) and power (β = 0.022; p = 0.022) interacted with lesser self-efficacy to predict worsening stair ascent performance. Strategies to sustain or improve mobility in women with knee osteoarthritis must focus on controlling pain and boosting self-efficacy. In those with worse self-efficacy, developing knee muscle capacity is an important target.

Purpose: To compare the acute effect of running and bicycling of an equivalent cumulative load on knee cartilage composition and morphometry in healthy young men. A secondary analysis investigated the relationship between activity history and the change in cartilage composition after activity., Methods: In fifteen men (25.8±4.2 years), the vertical ground reaction force was measured to determine the cumulative load exposure of a 15-min run. The vertical pedal reaction force was recorded during bicycling to define the bicycling duration of an equivalent cumulative load. On separate visits that were spaced on average 17 days apart, participants completed these running and bicycling bouts. Mean cartilage transverse relaxation times (T 2 ) were determined for cartilage on the tibia and weight-bearing femur before and after each exercise. T 2 was measured using a multi-echo spin-echo sequence and 3T MRI. Cartilage of the weight bearing femur and tibia was segmented using a highly-automated segmentation algorithm. Activity history was captured using the International Physical Activity Questionnaire., Results: The response of T 2 to bicycling and running was different (p=0.019; mean T 2 : pre-running=34.27ms, pre-bicycling=32.93ms, post-running=31.82ms, post-bicycling=32.36ms). While bicycling produced no change (-1.7%, p=0.300), running shortened T 2 (-7.1%, p<0.001). Greater activity history predicted smaller changes in tibial, but not femoral, T 2 ., Conclusions: Changes in knee cartilage vary based on activity type, independent of total load exposure, in healthy young men. Smaller changes in T 2 were observed after bicycling relative to running. Activity history was inversely related to tibial T 2 , suggesting cartilage conditioning., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Unlabelled: Accelerometers provide a measure of step-count. Reliability and validity of step-count and pedal-revolution count measurements by the GT3X+ accelerometer, placed at different anatomical locations, is absent in the literature. The purpose of this study was to investigate the reliability and validity of step and pedal-revolution counts produced by the GT3X+ placed at different anatomical locations during running and bicycling. Twenty-two healthy adults (14 men and 8 women) completed running and bicycling activity bouts (5 minutes each) while wearing 6 accelerometers: 2 each at the waist, thigh and shank. Accelerometer and video data were collected during activity. Excellent reliability and validity were found for measurements taken from accelerometers mounted at the waist and shank during running (Reliability: intraclass correlation (ICC) ≥ 0.99; standard error of measurement (SEM) ≤1.0 steps;, Validity: Pearson ≥ 0.99) and at the thigh and shank during bicycling (Reliability: ICC ≥ 0.99; SEM ≤1.0 revolutions;, Validity: Pearson ≥ 0.99). Excellent reliability was found between measurements taken at the waist and shank during running (ICC ≥ 0.98; SEM ≤1.6 steps) and between measurements taken at the thigh and shank during bicycling (ICC ≥ 0.99; SEM ≤1.0 revolutions). These data suggest that the GT3X+ can be used for measuring step-count during running and pedal-revolution count during bicycling. Only shank placement is recommended for both activities.