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Abstract-This paper proposes a new approach to automatically quantify the severity of knee osteoarthritis (OA) from radiographs using deep convolutional neural networks (CNN). Clinically, knee OA severity is assessed using Kellgren & Lawrence (KL) grades, a five point scale. Previous work on automatically predicting KL grades from radiograph images were based on training shallow classifiers using a variety of hand engineered features. We demonstrate that classification accuracy can be significantly improved using deep convolutional neural network models pre-trained on ImageNet and fine-tuned on knee OA images. Furthermore, we argue that it is more appropriate to assess the accuracy of automatic knee OA severity predictions using a continuous distance-based evaluation metric like mean squared error than it is to use classification accuracy. This leads to the formulation of the prediction of KL grades as a regression problem and further improves accuracy. Results on a dataset of X-ray images and KL grades from the Osteoarthritis Initiative (OAI) show a sizable improvement over the current state-of-the-art.

Cutting ability is an important aspect of many team sports, however, the biomechanical determinants of cutting performance are not well understood. This study aimed to address this issue by identifying the kinetic and kinematic factors correlated with the time to complete a cutting maneuver. In addition, an analysis of the test-retest reliability of all biomechanical measures was performed. Fifteen (n = 15) elite multidirectional sports players (Gaelic hurling) were recruited, and a 3-dimensional motion capture analysis of a 75° cut was undertaken. The factors associated with cutting time were determined using bivariate Pearson's correlations. Intraclass correlation coefficients (ICCs) were used to examine the test-retest reliability of biomechanical measures. Five biomechanical factors were associated with cutting time (2.28 ± 0.11 seconds): peak ankle power (r = 0.77), peak ankle plantar flexor moment (r = 0.65), range of pelvis lateral tilt (r = -0.54), maximum thorax lateral rotation angle (r = 0.51), and total ground contact time (r = -0.48). Intraclass correlation coefficient scores for these 5 factors, and indeed for the majority of the other biomechanical measures, ranged from good to excellent (ICC >0.60). Explosive force production about the ankle, pelvic control during single-limb support, and torso rotation toward the desired direction of travel were all key factors associated with cutting time. These findings should assist in the development of more effective training programs aimed at improving similar cutting performances. In addition, test-retest reliability scores were generally strong, therefore, motion capture techniques seem well placed to further investigate the determinants of cutting ability.

Abstract. This paper introduces a new approach to automatically quantify the severity of knee OA using X-ray images. Automatically quantifying knee OA severity involves two steps: first, automatically localizing the knee joints; next, classifying the localized knee joint images. We introduce a new approach to automatically detect the knee joints using a fully convolutional neural network (FCN). We train convolutional neural networks (CNN) from scratch to automatically quantify the knee OA severity optimizing a weighted ratio of two loss functions: categorical cross-entropy and mean-squared loss. This joint training further improves the overall quantification of knee OA severity, with the added benefit of naturally producing simultaneous multi-class classification and regression outputs. Two public datasets are used to evaluate our approach, the Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST), with extremely promising results that outperform existing approaches.

BackgroundClinical assessments and rehabilitation in athletic groin pain (AGP) have focused on specific anatomical structures and uniplanar impairments rather than whole body movement.ObjectiveTo examine the effectiveness of rehabilitation that targeted intersegmental control in patients with AGP and to investigate post rehabilitation changes in cutting biomechanics.MethodsTwo hundred and five patients with AGP were rehabilitated focusing on clinical assessment of intersegmental control, linear running and change of direction mechanics in this prospective case series. Hip and Groin Outcome Score (HAGOS) was the primary outcome measure. Secondary measures included pain-free return to play rates and times, pain provocation on squeeze tests and three-dimensional (3D) biomechanical analysis during a 110° cutting manoeuvre.ResultsFollowing rehabilitation, patients demonstrated clinically relevant improvements in HAGOS scores (effect size (ES): 0.6–1.7). 73% of patients returned to play pain-free at a mean of 9.9 weeks (±3.5). Squeeze test values also improved (ES: 0.49–0.68). Repeat 3D analysis of the cutting movement demonstrated reductions in ipsilateral trunk side flexion (ES: 0.79) and increased pelvic rotation in the direction of travel (ES: 0.76). Changes to variables associated with improved cutting performance: greater centre of mass translation in the direction of travel relative to centre of pressure (ES: 0.4), reduced knee flexion angle (ES: 0.3) and increased ankle plantar flexor moment (ES: 0.48) were also noted.ConclusionsRehabilitation focused on intersegmental control was associated with improved HAGOS scores, high rates of pain-free return to sporting participation and biomechanical changes associated with improved cutting performance across a range of anatomical diagnoses seen in AGP.