Knee osteoarthritis (OA) is a debilitating musculoskeletal disorder that causes functional disability. Automatic knee OA diagnosis has great potential of enabling timely and early intervention, that ...can potentially reverse the degenerative process of knee OA. Yet, it is a tedious task, concerning the heterogeneity of the disorder. Most of the proposed techniques demonstrated single OA diagnostic task widely based on Kellgren Lawrence (KL) standard, a composite score of only a few imaging features (i.e. osteophytes, joint space narrowing and subchondral bone changes). However, only one key disease pattern was tackled. The KL standard fails to represent disease pattern of individual OA features, particularly osteophytes, joint-space narrowing, and pain intensity that play a fundamental role in OA manifestation. In this study, we aim to develop a multitask model using convolutional neural network (CNN) feature extractors and machine learning classifiers to detect nine important OA features: KL grade, knee osteophytes (both knee, medial fibular: OSFM, medial tibial: OSTM, lateral fibular: OSFL, and lateral tibial: OSTL), joint-space narrowing (medial: JSM, and lateral: JSL), and patient-reported pain intensity from plain radiography.
We proposed a new feature extraction method by replacing fully-connected layer with global average pooling (GAP) layer. A comparative analysis was conducted to compare the efficacy of 16 different convolutional neural network (CNN) feature extractors and three machine learning classifiers.
Experimental results revealed the potential of CNN feature extractors in conducting multitask diagnosis. Optimal model consisted of VGG16-GAP feature extractor and KNN classifier. This model not only outperformed the other tested models, it also outperformed the state-of-art methods with higher balanced accuracy, higher Cohen's kappa, higher F1, and lower mean squared error (MSE) in seven OA features prediction.
The proposed model demonstrates pain prediction on plain radiographs, as well as eight OA-related bony features. Future work should focus on exploring additional potential radiological manifestations of OA and their relation to therapeutic interventions.
•A comparative analysis of popular deep neural networks for the stratification of knee OA radiographic features.•Utilization of pretrained CNNs and global average pooling (GAP) as feature extractors for automatic knee OA diagnosis.•Development of multitask deep hybrid learning models for multi-OA-feature diagnosis using machine learning classifiers.•Development of deep hybrid learning models for knee pain classification from plain radiography.
Summary
Obstructive sleep apnea is a chronic, sleep‐related breathing disorder, which is an independent risk factor for cardiovascular disease. The renin–angiotensin–aldosterone system regulates salt ...and water homeostasis, blood pressure, and cardiovascular remodelling. Elevated aldosterone levels are associated with excess morbidity and mortality. We aimed to analyse the influence and implications of renin–angiotensin–aldosterone system derangement in individuals with and without obstructive sleep apnea. We pooled data from 20 relevant studies involving 2828 participants (1554 with obstructive sleep apnea, 1274 without obstructive sleep apnea). The study outcomes were the levels of renin–angiotensin–aldosterone system hormones, blood pressure and heart rate. Patients with obstructive sleep apnea had higher levels of plasma renin activity (pooled wmd+ 0.25 95% confidence interval 0.04–0.46, p = 0.0219), plasma aldosterone (pooled wmd+ 30.79 95% confidence interval 1.05–60.53, p = 0.0424), angiotensin II (pooled wmd+ 5.19 95% confidence interval 3.11–7.27, p < 0.001), systolic (pooled wmd+ 5.87 95% confidence interval 1.42–10.32, p = 0.0098) and diastolic (pooled wmd+ 3.40 95% confidence interval 0.86–5.94, p = 0.0086) blood pressure, and heart rate (pooled wmd+ 3.83 95% confidence interval 1.57–6.01, p = 0.0009) compared with those without obstructive sleep apnea. The elevation remained significant (except for renin levels) when studies involving patients with resistant hypertension were removed. Sub‐group analysis demonstrated that levels of angiotensin II were significantly higher only among the Asian population with obstructive sleep apnea compared with those without obstructive sleep apnea. Body mass index accounted for less than 10% of the between‐study variance in elevation of the renin–angiotensin–aldosterone system parameters. Patients with obstructive sleep apnea have higher levels of renin–angiotensin–aldosterone system hormones, blood pressure and heart rate compared with those without obstructive sleep apnea, which remains significant even among patients without resistant hypertension.
Walking speed provides a good proxy for gait abnormalities as individuals with medical morbidities tend to walk slower than healthy subjects. The walking speed assessment can be utilized as a ...powerful predictor of health events, which are related to musculoskeletal disorder and mental disease. The expanding need to distinguish gait pattern of individual according to health status has driven various analytical methods such as observational and instrumented gait analysis methods in capturing the human movement. Significant advances in 3D-gait analysis system have enabled a myriad of studies that advance our understanding of gait biomechanics. However, the data samples obtained from this system are large, with high degrees of variability. Hence, developing a reliable approach to distinguish gait patterns specific to the underlying pathologies is of paramount importance. Through this study, we have proposed the use of a deep learning framework with recurrent neural network (RNN) to interpret human walking speed based on kinematic data, whereby RNN is capable for time series data processing. Nevertheless, this model can hardly learn long-range dependencies across time steps in a sequence due to vanishing gradient. In this study, an improved RNN integrated with NVIDIA CUDA® Deep Neural Network Library Long Short-Term Memory (cuDNN LSTM) is introduced. This model is capable to classify the gait patterns of different walking speeds from seventeen healthy subjects, with a total of 453 gait cycles. Gait kinematic parameters were employed as the input layer of the deep learning architecture based on RNN is integrated with cuDNN LSTM. Our proposed framework has achieved an accuracy of 97% to classify different speeds (slow, normal and fast). This study therefore presents a method towards establishing a powerful tool to translate machine learning for gait analysis into clinical practice, whereby automated classifications of gait pattern could now improve acuity of clinical diagnoses.
A higher prevalence of knee pain in Southeast Asian countries, compared with non-Asian countries, is an established fact. This article hypothesizes that this fact, combined with personal, cultural, ...and environmental factors, may influence attitudes toward illness and treatment-seeking behavior and adherence.
This study aimed to determine current attitudes, stigma, and barriers of women to the management of chronic knee pain and treatment in two Southeast Asian countries.
Fourteen semi-structured interviews explored female lived perceptions of chronic knee pain in Southeast Asia. Using a phenomenological reduction process, open-ended questions allowed participants to voice their perceptions of their experience of this knee condition. Particular foci were potential stigma associated with the perceptions of others, health-seeking attitudes, and attitudes toward exercise.
The shared experiences of managing chronic knee pain revealed the impact of their condition on participants' normality of life and their struggles with pain, limitations, and fear for the future. Key individual, interpersonal, organizational and community barriers and facilitators impacted the health seeking attitudes and engagement with conservative rehabilitation programmes.
Improved socio-cultural competency and consideration for an individuals' intersectional identity and interpersonal relationships are key to designing rehabilitation and conservative management solutions. Co-creating alternative pathways for rehabilitation for individuals that are more distant from health facilities may help reduce socio-cultural barriers at a community level.
GaInNAsSb p-i-n photodetectors on GaAs substrates capable of detecting wavelengths up to 1550 nm with a reduced dark current are presented in this letter. Responsivities of 0.18 A/W at 1300 nm and ...0.098 A/W at 1550 nm were achieved in devices with a ~0.5-μm-thick GaInNAsSb p-i-n epitaxial layer with 10% In, 4.08% N, and 4.4% Sb. The absorption coefficient (α) spectra show that α is intrinsically higher than that of the indirect-gap Ge layer but ~2.6 times lower than that reported for a In 0.53 Ga 0.47 As epitaxial layer at 1550 nm. The dark currents of the GaInNAsSb devices are found to be lower than not only those of the GaInNAs devices of a similar energy gap but also the state-of-the-art Ge/Si avalanche photodiodes. The lower dark currents in the GaInNAsSb devices compared with the GaInNAs devices can possibly be attributed to the reduction of defects in the Sb-containing epitaxial layer.
Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features ...is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren–Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future.
Knee osteoarthritis is a degenerative joint disease that induces chronic pain and disability. Bone morphological analysis is a promising tool to understand the mechanical aspect of this disorder. ...This study proposes a 2D bone morphological analysis using manually segmented bones to explore morphological features related to distinct pain conditions. Furthermore, six semantic segmentation algorithms are assessed for extracting femur and tibia bones from X-ray images. Our analysis reveals that the morphology of the femur undergoes significant changes in instances where pain worsens. Conversely, improvements in pain may not manifest pronounced alterations in bone shape. The few-shot-learning-based algorithm, UniverSeg, demonstrated superior segmentation results with Dice scores of 99.69% for femur and 99.60% for tibia. Regarding pain condition classification, the zero-shot-learning-based algorithm, CP-SAM, achieved the highest accuracy at 66% among all models. UniverSeg is recommended for automatic knee bone segmentation, while SAM models show potential with prompt encoder modifications for optimized outcomes. These findings highlight the effectiveness of few-shot learning for semantic segmentation and the potential of zero-shot learning in enhancing classification models for knee osteoarthritis diagnosis.
Existing artificial intelligence (AI) models for diagnosing knee osteoarthritis (OA) have faced criticism for their lack of transparency and interpretability, despite achieving medical-expert-like ...performance. This opacity makes them challenging to trust in clinical practice. Recently, explainable artificial intelligence (XAI) has emerged as a specialized technique that can provide confidence in the model's prediction by revealing how the prediction is derived, thus promoting the use of AI systems in healthcare. This paper presents the first survey of XAI techniques used for knee OA diagnosis. The XAI techniques are discussed from two perspectives: data interpretability and model interpretability. The aim of this paper is to provide valuable insights into XAI's potential towards a more reliable knee OA diagnosis approach and encourage its adoption in clinical practice.
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