Exploring deep learning capabilities in knee osteoarthritis case study for classification

This research study is devoted to the investigation of deep neural networks (DNN) for classification of the complex problem of knee osteoarthritis diagnosis. Osteoarthritis (OA) is the most common chronic condition of the joints revealing a variation in symptoms' intensity, frequency and pattern. A large number of features/factors need to be assessed for knee OA, mainly related with medical risks factors including advanced age, gender, hormonal status, body weight or size, family history of disease etc. The main goal of this research study is to implement deep neural networks as a new efficient machine learning approach for this classification task taking into account the large number of medical factors affecting OA. The potential of the proposed methodology was demonstrated by classifying different subgroups of control participants from self-reported clinical data and providing a category of knee OA diagnosis. The investigated subgroups were defined by gender, age and obesity. Furthermore, to validate the proposed deep learning methodology, a comparison analysis between the proposed DNN and some benchmark machine learning techniques recommended for classification was conducted and the results showed the effectiveness of deep learning in the diagnosis of knee OA. © 2019 IEEE

Application of machine intelligence for osteoarthritis classification: a classical implementation and a quantum perspective

Osteoarthritis is the most common form of arthritis in the knee that comes with a variation in symptoms’ intensity, frequency and pattern. Knee OA (KOA) is often diagnosed using invasive and expensive methods that can measure changes in joint morphology and function. Early and accurate identification of significant risk factors in clinical data is of vital importance in diagnosing KOA. A machine intelligence approach is proposed here to enable automated, non-invasive identification of risk factors from self-reported clinical data about joint symptoms, disability, function and general health. The proposed methodology was applied to recognize participants with symptomatic KOA or being at high risk of developing KOA in at least one knee. Different machine learning and deep learning algorithms were tested and compared in terms of multiple criteria e.g. accuracy, per class accuracy and execution time. Deep learning was proved to be the most effective in terms of accuracy with classification accuracies up to 86.95%, evaluated on data from the osteoarthritis initiative study. Insights about ten different feature subsets and their effect on classification accuracy are provided. The proposed methodology was also demonstrated in subgroups defined by gender and age. The results suggest that machine intelligence and especially deep learning may facilitate clinical evaluation, monitoring and even prediction of knee osteoarthritis. Apart from the classical implementation of the proposed methodology, a quantum perspective is also discussed highlighting the future application of quantum computers in OA diagnosis. © 2019, Springer Nature Switzerland AG

Prediction of pain in knee osteoarthritis patients using machine learning: Data from Osteoarthritis Initiative

Knee Osteoarthritis(KOA) is a serious disease that causes a variety of symptoms, such as severe pain and it is mostly observed in the elder people. The main goal of this study is to build a prognostic tool that will predict the progression of pain in KOA patients using data collected at baseline. In order to do that we leverage a feature importance voting system for identifying the most important risk factors and various machine learning algorithms to classify, whether a patient's pain with KOA, will stabilize, increase or decrease. These models have been implemented on different combinations of feature subsets, and results up to 84.3% have been achieved with only a small amount of features. The proposed methodology demonstrated unique potential in identifying pain progression at an early stage therefore improving future KOA prevention efforts. © 2020 IEEE

A Machine Learning workflow for Diagnosis of Knee Osteoarthritis with a focus on post-hoc explainability

Knee Osteoarthritis (KOA) is a multifactorial disease-causing joint pain, deformity and dysfunction. The aim of this paper is to provide a data mining approach that could identify important risk factors which contribute to the diagnosis of KOA and their impact on model output, with a focus on posthoc explainability. Data were obtained from the osteoarthritis initiative (OAI) database enrolling people, with nonsymptomatic KOA and symptomatic KOA or being at high risk of developing KOA. The current study considered multidisciplinary data from heterogeneous sources such as questionnaire data, physical activity indexes, self-reported data about joint symptoms, disability and function as well as general health and physical exams' data from individuals with or without KOA from the baseline visit. For the data mining part, a robust feature selection methodology was employed consisting of filter, wrapper and embedded techniques whereas feature ranking was decided on the basis of a majority vote scheme. The validation of the extracted factors was performed in subgroups employing seven well-known classifiers. A 77.88 % classification accuracy was achieved by Logistic Regression on the group of the first forty selected (40) risk factors. We investigated the behavior of the best model, with respect to classification errors and the impact of used features, to confirm their clinical relevance. The interpretation of the model output was performed by SHAP. The results are the basis for the development of easy-to-use diagnostic tools for clinicians for the early detection of KOA. © 2020 IEEE

Biomechanical effects on lower extremities in human-robot collaborative agricultural tasks

The present study pertains to a key aspect of human-robot collaborative systems which is usually underestimated, namely occupational health prolepsis. The aim of this investigation was to assess the biomechanical effects of manual symmetric load lifting related to a synergistic agricultural task that utilizes an unmanned ground vehicle to undertake the carriage of loads. Towards that goal, kinetic and kinematic data were collected from the lower extremities of thirteen experienced workers, by testing three different deposit heights (70, 80, 90 cm) corresponding to possible adjustments of the available agricultural robot. Moreover, the muscle activation levels of three lower extremity muscles and one trunk muscle were evaluated via a wireless electromyography system. Overall, the experimental findings revealed that the lower examined load height was associated with larger knee flexion moments and hip extension moments. Nevertheless, this height was related to lower activation mainly of the erectus spinae muscles. Finally, insignificant alterations were observed for the ankle joint as well as the activation levels of the other muscles. Consequently, a height equal to 90 cm is suggested, however, by avoiding extreme lumbar postures. The current results can be exploited for possible ergonomic interventions concerning the optimal deposit height of a robotic platform when a similar case is designed. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Biomechanics of sit-to-stand transition after muscle damage

The purpose of the study was to examine the effects of exercise-induced muscle damage on the biomechanics of the sit-to-stand transition (STST). Seventeen volunteers participated in an intense, eccentric based, muscle damage protocol of knee flexors and extensors via an isokinetic dynamometer. Kinematic and kinetic data were collected using a 10-camera optoelectronic system and a force plate 24 h before and 48 h after exercise. Statistical analysis showed significant differences in kinematic and kinetic parameters after exercise. Forty-eight hours after exercise, the strategy did change and the knee joint relative effort level increased significantly. Pelvic and hip kinematics, in conjunction with the knee extension joint moment, provided an efficient mechanism to support the participants' locomotor system during the STST. These results may be of great significance in designing supportive devices, as well as composing rehabilitation programs for young or elderly individuals, with various musculoskeletal pathologies. (C) 2012 Elsevier B. V. All rights reserved

Prediction of Injuries in CrossFit Training: A Machine Learning Perspective

CrossFit has gained recognition and interest among physically active populations being one of the most popular and rapidly growing exercise regimens worldwide. Due to the intense and repetitive nature of CrossFit, concerns have been raised over the potential injury risks that are associated with its training including rhabdomyolysis and musculoskeletal injuries. However, identification of risk factors for predicting injuries in CrossFit athletes has been limited by the absence of relevant big epidemiological studies. The main purpose of this paper is the identification of risk factors and the development of machine learning-based models using ensemble learning that can predict CrossFit injuries. To accomplish the aforementioned targets, a survey-based epidemiological study was conducted in Greece to collect data on musculoskeletal injuries in CrossFit practitioners. A Machine Learning (ML) pipeline was then implemented that involved data pre-processing, feature selection and well-known ML models. The performance of the proposed ML models was assessed using a comprehensive cross validation mechanism whereas a discussion on the nature of the selected features is also provided. An area under the curve (AUC) of 77.93% was achieved by the best ML model using ensemble learning (Adaboost) on the group of six selected risk factors. The effectiveness of the proposed approach was evaluated in a comparative analysis with respect to numerous performance metrics including accuracy, sensitivity, specificity, AUC and confusion matrices to confirm its clinical relevance. The results are the basis for the development of reliable tools for the prediction of injuries in CrossFit. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Global Distribution, Dispersal Patterns, and Trend of Several Omicron Subvariants of SARS-CoV-2 across the Globe

Our study aims to describe the global distribution and dispersal patterns of the SARS-CoV-2 Omicron subvariants. Genomic surveillance data were extracted from the CoV-Spectrum platform, searching for BA.1*, BA.2*, BA.3*, BA.4*, and BA.5* variants by geographic region. BA.1* increased in November 2021 in South Africa, with a similar increase across all continents in early December 2021. BA.1* did not reach 100% dominance in all continents. The spread of BA.2*, first described in South Africa, differed greatly by geographic region, in contrast to BA.1*, which followed a similar global expansion, firstly occurring in Asia and subsequently in Africa, Europe, Oceania, and North and South America. BA.4* and BA.5* followed a different pattern, where BA.4* reached high proportions (maximum 60%) only in Africa. BA.5* is currently, by Mid-August 2022, the dominant strain, reaching almost 100% across Europe, which is the first continent aside from Africa to show increasing proportions, and Asia, the Americas, and Oceania are following. The emergence of new variants depends mostly on their selective advantage, translated as enhanced transmissibility and ability to invade people with existing immunity. Describing these patterns is useful for a better understanding of the epidemiology of the VOCs’ transmission and for generating hypotheses about the future of emerging variants. © 2022 by the authors