Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks

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

Journalistic Role Performance in Australia During the COVID-19 Pandemic:Events, Media Systems and Journalistic Practice

This study analyses data gathered as part of an international comparative study of journalistic role performance during the first year of the COVID-19 pandemic. We situate role performance at the intersection between anterior factors that shape journalistic decision-making and practice, and the contingent events and issues journalists are tasked with communicating. Based on this, we ground our analysis by considering (a) how news is shaped by media systems, and how Australia’s media system may be characterised; (b) studies of journalists’ work during previous health crises; and (c) analyses of media coverage of the COVID-19 pandemic. Our analysis focuses, firstly, on whether role performance in Australia’s 2020 news coverage was discernibly “consensus-based”; and, secondly, on whether there were any indicators of Australian coverage being “polarised” during this period. Our findings suggest role performance in 2020 was broadly reflective of a relative political consensus and that evidence of polarisation was limited. We find, nevertheless, that there were notable differences between different mediums and outlets, and reflect on factors that may have contributed to such differences. In light of this, we emphasise the importance of taking account of the relationship between local contexts and historical contingency in considering how role performances are produced.</p

News Representation and Sense of Belonging Among Multicultural Audiences

This study seeks to understand the role of representation in news media, trust in news, and participation in multicultural audiences' sense of belonging to society. A multimodal survey combining online, CATI, and CAPI methods was conducted in Australia at the end of 2021 and early 2022 (N = 1,084). The top five non-English language communities in Australia (Arabic, Cantonese, Italian, Mandarin, and Vietnamese) were included in the survey, of which n = 851 were born overseas. The findings reveal a significant link between the perception of sufficient representation in Australian news media, trust in news, confidence to participate in society, and sense of belonging. When multicultural audiences see themselves fairly and adequately represented in the news, they are more likely to trust the news and participate in the community by discussing the news and current affairs. This, in turn, leads to a stronger sense of belonging to society. We also found confidence in English and time spent in Australia were important factors contributing to perceptions of representation. While the length of stay has a positive impact on the perception of representation among those with high confidence in English, this perception is significantly lower among those who have lower confidence. This result confirms the significant role language proficiency plays in migrants' experiences in the host country

Monitoring Wellness, Training Load, and Running Performance During a Major International Female Field Hockey Tournament

The current observational study quantified players\u27 activity profiles during a major international female field hockey tournament and determined whether an association exists between well-being measures and running performance within elite female hockey players. Elite female field hockey players (23 6 3 years; 162.6 6 13 cm; and 66 6 6 kg) participated in the study. Participants running performance was monitored using global positioning system technology (S5; Catapult Innovations , Scoresby, Victoria, Australia), with daily well-being questionnaires used to quantify player responses during the tournament. Thresholds for the magnitude of the observed change for each variable were determined using the Hopkins Spreadsheets for analysis of controlled trials. Relative distance (m$min 21) was likely lower when compared with game 1 in game 7. Relative high speed (m$min 21 .16 km$h 21) was likely lower in games 5, 6, and 7 when compared with game 1. Subjective load was very likely higher in game 2 and very likely lower in game 3 when compared with game 1. Mood and sleep quality were likely lower in game 1 when compared with game 4 and game 7. Muscle soreness was likely higher when compared with game 1 in game. During the tournament, it was observed that a decrease in players\u27 daily well-being was accompanied by changes in running performance. Furthermore , changes to players\u27 muscle soreness and sleep quality result in decreased players\u27 high-speed running performance during match-play. Therefore, to prevent the observed effects, coaches should adopt strategies to enhance sleep quality and incorporate specific recovery modalities to reduce musculo-skeletal soreness

Fat quantification in MRI-defined lumbar muscles

Some studies suggest fat infiltration in the lumbar muscles (LM) is associated with lower back pain (LBP) in adults. Usually fat in MRI-defined lumbar muscles is qualitatively valuated by visual grading via a 3 point scale, whereas a quantitative continuous (0 - 100%) approach may provide a greater insight. In this paper, we propose a method to precisely quantify the fat content / infiltration in a user-defined region of the lumbar muscles, which may aid better diagnosis. The key steps are segmenting the region of interest (ROI) from the lumbar muscles, identifying the fatty regions in the segmented region based on the selected threshold and softness levels, computing the parameters (such as total and region-wise fat content percentage, total-cross sectional area (TCSA), functional cross- sectional area (FCSA)) and exporting the computations and associated patient information from the MRI, into a atabase. A standalone application using MATLAB R2010a was developed to perform the required computations along with an intuitive GUI

Predicting knee osteoarthritis severity: comparative modeling based on patient's data and plain X-ray images

Knee osteoarthritis (KOA) is a disease that impairs knee function and causes pain. A radiologist reviews knee X-ray images and grades the severity level of the impairments according to the Kellgren and Lawrence grading scheme; a five-point ordinal scale (0-4). In this study, we used Elastic Net (EN) and Random Forests (RF) to build predictive models using patient assessment data (i.e. signs and symptoms of both knees and medication use) and a convolution neural network (CNN) trained using X-ray images only. Linear mixed effect models (LMM) were used to model the within subject correlation between the two knees. The root mean squared error for the CNN, EN, and RF models was 0.77, 0.97 and 0.94 respectively. The LMM shows similar overall prediction accuracy as the EN regression but correctly accounted for the hierarchical structure of the data resulting in more reliable inference. Useful explanatory variables were identified that could be used for patient monitoring before X-ray imaging. Our analyses suggest that the models trained for predicting the KOA severity levels achieve comparable results when modeling X-ray images and patient data. The subjectivity in the KL grade is still a primary concern