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Bag-of-Visual Words (BoVW) and deep learning techniques have been widely used in several domains, which include computer-assisted medical diagnoses. In this work, we are interested in developing tools for the automatic identification of Parkinson’s disease using machine learning and the concept of BoVW. The proposed approach concerns a hierarchical-based learning technique to design visual dictionaries through the Deep Optimum-Path Forest classifier. The proposed method was evaluated in six datasets derived from data collected from individuals when performing handwriting exams. Experimental results showed the potential of the technique, with robust achievements.
Parkinson's disease (PD) is a neurodegenerative disease that affects millions of people worldwide, causing mental and mainly motor dysfunctions. The negative impact on the patient's daily routine has moved the science in search of new techniques that can reduce its negative effects and also identify the disease in individuals. One of the main motor characteristics of PD is the hand tremor faced by patients, which turns out to be a crucial information to be used towards a computer-aided diagnosis. In this context, we make use of handwriting dynamics data acquired from individuals when submitted to some tasks that measure abilities related to writing skills. This work proposes the application of recurrence plots to map the signals onto the image domain, which are further used to feed a Convolutional Neural Network for learning proper information that can help the automatic identification of PD. The proposed approach was assessed in a public dataset under several scenarios that comprise different combinations of deep-based architectures, image resolutions, and training set sizes. Experimental results showed significant accuracy improvement compared to our previous work with an average accuracy of over 87%. Moreover, it was observed an improvement in accuracy concerning the classification of patients (i.e., mean recognition rates above to 90%). The promising results showed the potential of the proposed approach towards the automatic identification of Parkinson's disease.
This article highlights methodological and ethical challenges in research with adults of older and oldest age, by presenting field experiences of the current research project “Motion Monitoring of Geriatric Trauma Patients - Explorative Study on the Rehabilitation Process after Hip Fracture Using Sensor-based Data”. Depiction of the survey situation, with regard to the subjects in particular, can serve as practical examples for designing future research projects.
The group of older adults is a rather large and growing group for which research is required, especially concerning their heterogeneity, their individual autonomy and quality of life. It is assumed, that research designs of studies on the target group must be specifically adjusted, in particular when considering the attribution of vulnerability of the group members. At the same time, it is not clear yet what exact specifics of the subjects and target group must be considered in research designs, as surprisingly little is known about the target group as subjects and corresponding theories have been insufficiently tested.
The exploratory long-term design of the research project presented in the second section of this chapter has a positive evaluation of an ethics committee. Still ethical challenges occurred in the field situation, that are illustrated in the third section of this chapter, by providing information on the patients, their role as research subjects, how they were recruited, how an informed consensus was reached, and in some cases how participation was rejected or abandoned. After a summary, the end of the paper is marked by recommendations on how to design future research projects.
Cumulatively it must always be expected that interaction between researchers and research subjects of this target group can become very intensive, what requires to follow clearly defined procedures and at the same time to be prepared to act flexibly.
Pediatric patients suffering from ependymoma are usually treated with cranial or craniospinal three-dimensional (3D) conformal radiotherapy (3DCRT). Intensity-modulated techniques spare dose to the surrounding tissue, but the risk for second malignancies may be increased due to the increase in low-dose volume. The aim of this study is to investigate if the flattening filter free (FFF) mode allows reducing the risk for second malignancies compared to the mode with flattening filter (FF) for intensity-modulated techniques and to 3DCRT. A reduction of the risk would be advantageous for treating pediatric ependymoma. 3DCRT was compared to intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT) with and without flattening filter. Dose-volume histograms (DVHs) were compared to evaluate the plan quality and used to calculate the excess absolute risk (EAR) to develop second cancer in the brain. Dose verification was performed with a two-dimensional (2D) ionization chamber array and the out-of-field dose was measured with an ionization chamber to determine the EAR in peripheral organs. Delivery times were measured. Both VMAT and IMRT achieved similar plan quality in terms of dose sparing in the OAR and higher PTV coverage as compared to 3DCRT. Peripheral dose in low-dose region, which is proportional to the EAR in organs located in this region, for example, gonads, bladder, or bowel, could be significantly reduced using FFF. The lowest peripheral EAR and lowest delivery times were hereby achieved with VMATFFF . The EAR calculated based on DVH in the brain could not be reduced using FFF mode. VMATFFF improved the target coverage and homogeneity and kept the dose in the OAR similar compared to 3DCRT. In addition, delivery times were significantly reduced using VMATFFF . Therefore, for radiotherapy of ependymoma patients, VMATFFF may be considered advantageous for the combination of Elekta Synergy linac and Oncentra External Beam planning system used in this study.
Against the background of the huge rise in the number of asylum seekers and refugees absorbed into German society over the last couple of years this article examines the innovative Bavarian programme of preparatory classes for asylum seekers between 16 and 21 years of age. The challenges faced by educational and social work professionals working with this target group are documented alongside the particular resources which these highly-motivated young people as a rule bring with them. Specific requirements for psychotherapy professionals are addressed as well as a range of recommendations formulated by the author to enable the longer-term success of this vocational training programme in integrating these young people into the German educational and employment system. It has become apparent that a holistic approach to the lives of the target group by professionals in education, social work and therapy is necessary to enable young asylums seekers to complete vocational training successfully. Transitional management is a key part of the process of setting asylum seekers on the path to an apprenticeship. Coordination with the Labour Office, local authority administration and local decision-makers are further parts of the equation. This must intermesh with civil society engagement to provide the maximum use of resources giving young refugees access to training, the labour market and long term integration into German life.
Objective: Artificial intelligence (AI) may reduce underdiagnosed or overlooked upper GI (UGI) neoplastic and preneoplastic conditions, due to subtle appearance and low disease prevalence. Only disease-specific AI performances have been reported, generating uncertainty on its clinical value.
Design: We searched PubMed, Embase and Scopus until July 2020, for studies on the diagnostic performance of AI in detection and characterisation of UGI lesions. Primary outcomes were pooled diagnostic accuracy, sensitivity and specificity of AI. Secondary outcomes were pooled positive (PPV) and negative (NPV) predictive values. We calculated pooled proportion rates (%), designed summary receiving operating characteristic curves with respective area under the curves (AUCs) and performed metaregression and sensitivity analysis.
Results: Overall, 19 studies on detection of oesophageal squamous cell neoplasia (ESCN) or Barrett's esophagus-related neoplasia (BERN) or gastric adenocarcinoma (GCA) were included with 218, 445, 453 patients and 7976, 2340, 13 562 images, respectively. AI-sensitivity/specificity/PPV/NPV/positive likelihood ratio/negative likelihood ratio for UGI neoplasia detection were 90% (CI 85% to 94%)/89% (CI 85% to 92%)/87% (CI 83% to 91%)/91% (CI 87% to 94%)/8.2 (CI 5.7 to 11.7)/0.111 (CI 0.071 to 0.175), respectively, with an overall AUC of 0.95 (CI 0.93 to 0.97). No difference in AI performance across ESCN, BERN and GCA was found, AUC being 0.94 (CI 0.52 to 0.99), 0.96 (CI 0.95 to 0.98), 0.93 (CI 0.83 to 0.99), respectively. Overall, study quality was low, with high risk of selection bias. No significant publication bias was found.
Conclusion: We found a high overall AI accuracy for the diagnosis of any neoplastic lesion of the UGI tract that was independent of the underlying condition. This may be expected to substantially reduce the miss rate of precancerous lesions and early cancer when implemented in clinical practice.
Soccer is one of the most popular sports all around the world. It is an injurious type of sport with a focus on lower extremities and high psychological pressure during matches. The stressor is linked with injuries and an increased musculoskeletal loading. This study investigates the influence of cognitive stress on the load profile of the knee joint. Twelve professional youth soccer players performed highly dynamic runs with and without additional cognitive stress. The runs were analysed with a musculoskeletal simulation software. The data analysis shows no difference in knee joint reaction loading under additional mental stress compared to the baseline. Yet running times are significantly lower in the baseline. While there is no increase in the joint loads, the running times indicate an altered movement behaviour when the subjects are exposed to additional mental demand.
Mental stress reduces performance and changes musculoskeletal loading in football-related movements
(2020)
Purpose:
Football players have a high risk of leg muscle injuries, especially when exposed to mental stress. Hence, this study investigated the musculoskeletal response of elite youth football players during highly dynamic movements under stress. The hypothesis is that mental stress reduces performance and changes the muscular forces exerted.
Materials & methods:
Twelve elite youth football players were subjected to mental stress while performing sports-specific change-of-direction movements. A modified version of the d2 attention test was used as stressor. The kinetics are computed using inverse dynamics. Running times and exerted forces of injury-prone muscles were analysed.
Results:
The stressor runs were rated more mentally demanding by the players (p = 0.006, rs = 0.37) with unchanged physical demand (p = 0.777, rs = 0.45). This resulted in 10% longer running times under stress (p < 0.001, d = −1.62). The musculoskeletal analysis revealed higher peak muscle forces under mental stress for some players but not for others.
Discussion:
The study shows that motion capture combined with musculoskeletal computation is suitable to analyse the effects of stress on athletes in highly dynamic movements. For the first time in football medicine, our data quantifies an association between mental stress with reduced football players’ performance and changes in muscle force.
Exoskeletons were invented over 100 years ago but have only become popular in the last two decades, especially in the working industry as they can decrease work-related loads significantly. The most often used exoskeletons are for the lower back and shoulder since these are commonly affected body regions. All devices have in common that their purpose is to reduce internal loads of vulnerable body regions. Nevertheless, there is still little understanding on how biomechanical loading in the human body changes when exoskeletons are used. Therefore, further analyses are needed. A promising candidate for these are musculoskeletal models, which are based on an inverse dynamics approach and can calculate external parameters such as ground reaction forces or other interaction forces as well as internal parameters such as joint reaction forces or muscle activities. The various examples in the literature show that these models are increasingly used for assessing the biomechanical effects of exoskeletons on the human body. Furthermore, musculoskeletal models can calculate biomechanical loadings of humans with and without exoskeletons for all kinds of applications and allow an evaluation of their purpose.
Practical Relevance: This article highlights the possibilities of musculoskeletal models for assessing the design and efficiency of occupational exoskeletons. Several practical use cases are described along with distinct descriptions of common implications of musculoskeletal and exoskeleton modeling.