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Physical inactivity is becoming an important threat to public health in today's society. The COVID-19 pandemic has also reduced physical activity (PA) levels given all the restrictions imposed worldwide. In this work, physical activity interventions supported by mobile devices and relying on control engineering principles were proposed. The model was constructed relying on previous studies that consider a fluid analogy of Social Cognitive Theory (SCT), which is a psychological theory that describes how people acquire and maintain certain behaviors, including health-promoting behaviors, through the interplay of personal, environmental, and behavioral factors. The obtained model was validated using secondary data (collected earlier) from a real intervention with a group of male subjects in Great Britain. The present model was extended with new technology for a better understanding of behavior change interventions. This involved the use of applications, such as phone-based ecological momentary assessments, to collect behavioral data and the inclusion of simulations with logical reward conditions for reaching the behavioral threshold. A goal of 10,000 steps per day is recommended due to the significant link observed between higher daily step counts and lower mortality risk. The intervention was designed using a Model Predictive Control (MPC) algorithm configured to obtain a desired performance. The system was tested and validated using simulation scenarios that resemble different situations that may occur in a real setting.
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The advance of digitalization is constantly bringing new solutions to various areas of life in our society. The COVID-19 pandemic, among other things, brought increased attention to the application and support of treatments through digital solutions in the healthcare sector due to contact restrictions. However, the development of digital solutions comes at a high cost in terms of time and expenses. Mobile app development requires the development of two separate apps for the two respective market-leading mobile operating systems iOS and Android. Cross-platform frameworks make it possible to develop apps for both operating systems on a single code base, thus saving the development and maintenance of two separate codes. Flutter is currently the most popular cross-platform framework for the development of mobile apps. This paper has evaluated Flutter based on an existing criteria catalogue. As a usage context for the evaluation, a prototype for Cancer Counselling App of the University Medical Center Freiburg was implemented. According to the gained own prototyping experience with Flutter and a thorough literature analysis in this area, the criteria catalogue was filled out and the result was compared with other mobile App development paradigms. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
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The Covid-19 global pandemic has represented a challenge for education, which had to migrate to virtual environments. Universities adopted different teaching methods to keep contributing to the growth of the professionals in various fields. In this context, the Biomedical Engineering program of the Pontificia Universidad Catolica del Peru and the Universidad Peruana Cayetano Heredia had to change or adapt the methodology of the courses included in its curriculum in order to reach the learning objectives. This paper presents a methodology for an innovative approach of simulated scenarios using digital tools for the virtual teaching of Clinical Engineering. The learning results achieved in two semesters of implementation of the methodology, during 2020 and 2021, were measured by means of a survey applied to the students at the end of the course. Obtaining achievement results above 76 % and improvement opportunities that would be useful for the next version of this course and for the replication of the methodology in other universities. © 2023 IEEE.
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At the beginning of the new year of 2020, the new coronavirus swept across the motherland. Scientific researchers throughout the country overcame scientific difficulties and wrote papers for the motherland. Under the challenge of the epidemic situation, medical workers in the front saved the dying and healed the wounded, while the scientific researchers in the rear tackled scientific and technological problems. The two sides cooperated sincerely to unify medical practice and theoretical development, and effectively improved the scientific and technological level of China's medical industry. Health care is closely related to human survival, development and quality of life. At present, mankind is still facing the threat of major diseases, and the development of medical and health services has increasingly shown strategic significance to national security, social stability and even national survival. This article introduces a new model of talent cultivation at the graduate level. The cross-dissolution of clinical medicine and manufacturing engineering produces novel ideas and new technologies.
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Traditional Chinese medicine (TCM) has significant benefits in the prevention and treatment of diseases due to its unique theoretical system and research techniques. However, there are still key issues to be resolved in the full interpretation and use of TCM, such as vague active compounds and mechanism of action. Therefore, it is promising to promote the research on TCM through innovative strategies and advanced cutting-edge technologies. Microfluidic chips have provided controllable unique platforms for biomedical applications in TCM research with flexible composition and large-scale integration. In this review, the analysis and biomedical applications of microfluidics in the field of TCM are highlighted, including quality control of Chinese herbal medicines (CHMs), delivery of CHMs, evaluation of pharmacological activity as well as disease diagnosis. Finally, potential challenges and prospects of existing microfluidic technologies in the inheritance and innovation of TCM are discussed.Copyright © 2022 Elsevier B.V.
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With the developments of medical artificial intelligence (AI), meta-data analysis, intelligence-aided drug design and discovery, surgical robots and image-navigated precision treatments, intelligent medicine (IM) as a new era evolved from ancient medicine and biomedical medicine, has become an emerging topic and important criteria for clinical applications. It is fully characterized by fundamental research-driven, new-generation technique-directed as well as state-of-the-art paradigms for advanced disease diagnosis and therapy leading to an even broader future of modern medicine. As a fundamental subject and also a practice-oriented field, intelligent medicine is highly trans-disciplinary and cross-developed, which has emerged the knowledge of modern medicine, basic sciences and engineering. Basically, intelligent medicine has three domains of intelligent biomaterials, intelligent devices and intelligent techniques. Intelligent biomaterials derive from traditional biomedical materials, and currently are endowed with multiple functionalities for medical uses. For example, micro-/nanorobots, smart responsive biomaterials and digital drugs are representative intelligent biomaterials which have been already commercialized and applied to clinical uses. Intelligent devices, such as surgical robots, rehabilitation robots and medical powered exoskeleton, are an important majority in the family of intelligent medicine. Intelligent biomaterials and intelligent devices are more and more closely integrated with each other especially on the occasions of intelligence acquisition, remote transmission, AI-aided analysis and management. In comparison, intelligent techniques are internalized in the former two domains and are playing a critical role in the development of intelligent medicine. Representative intelligent techniques of telemedicine, image-navigated surgery, virtual/augmented reality and AI-assisted image analysis for early-stage disease assessments have been employed in nowadays clinical operations which to a large extent relieved medical labors. In the past decades, China has been in the leading groups compared to international colleagues in the arena of intelligent medicine, and a series of eminent research has been clinically translated for practical uses in China. For instance, the first 5G-aided remote surgery has been realized in Fujian Province in January 2019, which for the first time validated their applicability for human uses. The surgical robots have found China as the most vigorous market, and more than 10 famous Chinese companies are developing versatile surgical robots for both Chinese people and people all over the world. China also applied AI techniques to new drug developments especially in early 2020 when COVID-19 epidemic roared, and several active molecules and drug motifs have been discovered for early-stage COVID-19 screening and treatments. Based on the significance of intelligent medicine and its rapid developments in both basic research and industrials, this review summarized the comprehensive viewpoints of the Y6 Xiangshan Science Conferences titled with Fundamental Principles and Key Technologies of Intelligent Medicine, and gave an in-depth discussion on main perspectives of future developments of the integration of biomaterial and devices, the integration of bioinformatics and medical hardware, and the synergy of biotechnology and intelligence information. It is expected that this featuring article will further promote intelligent medicine to an even broader community not only for scientists but also for industrials, and in the long run embrace a perspective future for its blooming and rich contributions in China in the coming 5 years. © 2023 Chinese Academy of Sciences. All rights reserved.
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Although intensive care medicine (ICM) is a relatively young discipline, it has rapidly developed into a full-fledged and highly specialized specialty covering several fields of medicine. The COVID-19 pandemic led to a surge in intensive care unit demand and also bring unprecedented development opportunities for this area. Multiple new technologies such as artificial intelligence (AI) and machine learning (ML) were gradually being applied in this field. In this study, through an online survey, we have summarized the potential uses of ChatGPT/GPT-4 in ICM range from knowledge augmentation, device management, clinical decision-making support, early warning systems, and establishment of intensive care unit (ICU) database.
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Purpose: As the COVID-19 emergency evolved, a wide range of 'new' technology based solutions were offered to meet clinical and occupational health needs in Europe. This technology extended beyond the standard medical devices usually deployed in clinical settings, and therefore required rapid assessment of suitability for use in hospitals. Here we describe a hospital-based COVID-19 technology assessment service (www.misa.ie/researchdevelopment/ bioengineering-lab/technology-assessment) that was developed and share our experience of its implementation. Material(s) and Method(s): A scientifically grounded assessment service was established to evaluate specific technological solutions. This service was led by a team of 2 Senior Medical Physicists and 1 Senior Clinical Engineer, with each assessment drawing on pan-hospital expertise and a specialist technology evaluation infrastructure. Each solution was evaluated using a standardized agile process: 1) user centric needs assessment;2) applicable literature and international standards review;3) balanced risk-benefit assessment;4) initial device functionality and usability assessment;5) in-depth device technical testing and safety assessment;6) rapid communications and detailed reporting;7) support for local clinical implementation/ installation with on-going evaluation. Evaluations were described in the form of short Bulletins with a webpage developed to share these findings internationally. Result(s): To date, a diverse range of technological systems and innovative solutions were evaluated, including thermal cameras for mass temperature screening, baby monitor devices for isolation room communications, augmented reality systems, a varied range of thermometers, and connected health technologies for remote working and clinical testing. Substantial variability in quality and standard of systems on offer was identified, with potential patient risks highlighted and mitigated. Critical success factors of the assessment service identified include: a central focus on the impact of solutions on both patients and staff, accessible local scientific and technical expertise supporting real-world testing and user feedback, an agile process which was responsive to high levels of uncertainty and a rapid communications process that was adaptive, responsive and connected both locally and nationally. Conclusion(s): Emergency situations, while challenging, are a huge stimulus for healthcare system-wide changes where barriers to technological innovation are significantly reduced, providing significant opportunities for adoption of new and innovative solutions. While there is a need for timely and practical technology assessments during an acute emergency, these should still be grounded in well-established scientific and safety principles that prioritize the health and safety of patients, staff and the public. A hospital-based COVID-19 technology assessment service has provided a practical and successful solution to this challenge.Copyright © 2023 Southern Society for Clinical Investigation.
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This study evaluates a hybrid training program focusing on soft skills development for freshly graduated trainees in a multinational biomedical engineering company. The evaluation is conducted as a diagnostic tool to measure the performance of trainees that feeds the return- on- investment (ROI) of the assigned company. We implement an evaluative case study with both quantitative and qualitative methods;- learning analytics and semi-structured interviews. The quantitative data comes from the learning analytics of twenty-nine trainees, while qualitative data comes from five interviewees from the same cohort. We found that the trainees' negative learning experiences are traced to an inadequate time to participate online and a perception of irrelevant online training content. These resulted in lower performance scores and engagement during online sessions compared to face-to-face sessions. The respondents also stated that face-to-face sessions allow in-person interaction with trainers. This factor led to a positive learning experience and potentially heightened engagement that directed to a higher performance score. Instructional design considerations in planning effective hybrid training are deliberately discussed for future practitioners and researchers. © 2022 IEEE.
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With the Internet of Things and medical technology development, patients use wearable telemedicine devices to transmit health data to hospitals. The need for data sharing for public health has become more urgent under the COVID-19 pandemic. Previously, security protection technology was difficult to solve the increasing security risks and challenges of telemedicine. To address the above hindrances, Federated learning (FL) solves the difficulty for companies and institutions to share user data securely. The global server iterative aggregates the model parameters from the local server instead of uploading the user's data directly to the cloud server. We propose a new model of federated distillation learning called FedTD, which allows the different models between local hospital servers and global servers. Unlike traditional federated learning, we combine the knowledge distillation method to solve the non-Independent Identically Distribution (non-IID) problem of patient medical data. It provides a security solution for sharing patients' medical information among hospitals. We tested our approach on the COVID-19 Radiography and COVID-Chestxray datasets to improve the model performance and reduce communication costs. Extensive experiments show that our FedTD significantly outperforms the state-of-the-art. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Aim: The satisfaction of proffering radiological health care considerably contributes to the improvement and quality of health care in health care institutions, especially in the extraordinary situations, such as the viral Coronavirus disease infection where patients are being treated under special conditions. Very often, the problem regarding the understanding among the patients and the healthcare workers can occur. The primary purpose of the research is to determine the contentment of infected Covid-19 patients with the provided service of the radiological diagnostics department. Method(s): The study was conducted at the Public Health Institution Brcko District Health Center" (Dispensary for lung diseases) and TBC (ATD). The number of partic-ipants was 700. The questionnaire consisted of 19 questions. Laschinger's customised and standardized questionnaire called HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems), a questionnaire about the patient's contentment with the quality of health care, was used for the purpose of research analysis(1). The Pearson correlation coefficient and the ANOVA test were used for statistical data pro-cessing. Result(s): The given results have shown that the patients rated the quality of health care with a number 4 which indicates that they are somewhat satisfied with the provided quality. When asked about level of adequate care during their imaging, patients were less satisfied with nurses and technicians in comparison to the medical radiology en-gineers. Conclusion(s): The obtained results can contribute to a better understanding of patients as well as the improvement of health systems in the form of new health care reforms, as well as to make progress in educating health workers how to communicate with patients.Copyright © 2019 by Acta Medica Saliniana.
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Antimicrobial biocompatible polymers have highly desirable materials in medical technology to prevent any healthcare-associated infections from the in-dwelling on medical device that could pose a risk to patients, and this is gaining attention particularly in the context of the current COVID-19 pandemic. In this study, ester free type of poly(trimethylene carbonate) (PTMC) derivatives containing cinnamyl group has been synthesized. Also, several of alcohol-based initiators;benzyl alcohol, 2,2-dimethyl-1-propanol, 4-(2-hydroxyethoxy) benzaldehyde and 2-(p-tolyloxy)ethan-1-ol and polymer structure has been designed and analyzed its molecular weight on thermal and physical properties effect. The homopolymers and copolymers of PTMC derivatives with cinnamyl group had achieved with molecular weight up to 7400 — 12,300 g/mol with the improved physical state and glass transition temperature, Tg at 8 ֩C respectively. Different types of essential oil, which is thymol and carvacrol, have been incorporated with cinnamyl PTMC had resulted the suppression of E.coli and S.aureus growth. The results showed a promising of introduction of cinnamyl derivatives into ester free-type of PTMC derivatives, as well as create more alcohol-initiators for polymerization of trimethylene carbonate, for more advanced development in the future. © 2023 Elsevier B.V.
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In the context of so much uncertainty with coronavirus variants and official mandate based on seemingly exaggerated predictions of gloom from epidemiologists, it is appropriate to consider a revised model of relative simplicity, because there can be dangers in developing models which endeavour to account for too many variables. Predictions and projections from any such models have to be in the context of relevant contingencies. The model presented here is based on relatively simple second order difference equations. The context here is as important as the content in that in many Western counties where the narrative currently seems more important than the truth, and the results of empirical science are valued more as a shield for politicians than a sword for protection of citizens © 2022 by the authors. Licensee Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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BackgroundAfter its emergence in Wuhan in December 2019, the COVID-19 virus disseminated around the globe creating an incredible panic and outweighing the healthcare system's capacities. As a result of this hazardous situation, healthcare workers were placed at an unprecedented challenge making them vulnerable to a wide spectrum of mental health issues including anxiety and depression. This was a cross-sectional health facility-based study aiming to explore the psychological impact of the COVID-19 pandemic on Sudanese healthcare workers in COVID-19 treatment centers. Convenience sampling was applied. And two-part self-administered online questionnaire was used: the first part consisted of the demographic data and concerns related to COVID-19, and the second part consisted of the Hospital Anxiety and Depression Scale (HADS) standardized questionnaire. Ethical clearance was obtained from the Ministry of Health.ResultsOne-hundred thirty-three healthcare workers of different job titles were enrolled in this study. A total of 54% were females, and 46% were males. Most of the participants had borderline depression (44%) or anxiety scores (47%). Females had significantly a higher mean anxiety score than males. There was no significant difference between participants with and without chronic disease on both anxiety and depression scores.ConclusionsHealthcare workers at the forefront of the fight against COVID-19 pandemic are facing critical mental health challenges. An immediate intervention from the decision-makers is needed to mitigate this deleterious impact and to strengthen the healthcare system's capacity in the face of healthcare emergencies in general.
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Background: and ObjectivIn the current COVID-19 outbreak, efficient testing of COVID-19 individuals has proven vital to limiting and arresting the disease's accelerated spread globally. It has been observed that the severity and mortality ratio of COVID-19 affected patients is at greater risk because of chronic pulmonary diseases. This study looks at radiographic examinations exploiting chest X-ray images (CXI), which have become one of the utmost feasible assessment approaches for pulmonary disorders, including COVID-19. Deep Learning(DL) remains an excellent image classification method and framework; research has been conducted to predict pulmonary diseases with COVID-19 instances by developing DL classifiers with nine class CXI. However, a few claim to have strong prediction results; because of noisy and small data, their recommended DL strategies may suffer from significant deviation and generality failures. Methods: Therefore, a unique CNN model(PulDi-COVID) for detecting nine diseases (atelectasis, bacterial-pneumonia, cardiomegaly, covid19, effusion, infiltration, no-finding, pneumothorax, viral-Pneumonia) using CXI has been proposed using the SSE algorithm. Several tranfer-learning models: VGG16, ResNet50, VGG19, DenseNet201, MobileNetV2, NASNetMobile, ResNet152V2, DenseNet169 are trained on CXI of chronic lung diseases and COVID-19 instances. Given that the proposed thirteen SSE ensemble models solved DL's constraints by making predictions with different classifiers rather than a single, we present PulDi-COVID, an ensemble DL model that combines DL with ensemble learning. The PulDi-COVID framework is created by incorporating various snapshots of DL models, which have spearheaded chronic lung diseases with COVID-19 cases identification process with a deep neural network produced CXI by applying a suggested SSE method. That is familiar with the idea of various DL perceptions on different classes. Results: PulDi-COVID findings were compared to thirteen existing studies for nine-class classification using COVID-19. Test results reveal that PulDi-COVID offers impressive outcomes for chronic diseases with COVID-19 identification with a 99.70% accuracy, 98.68% precision, 98.67% recall, 98.67% F1 score, lowest 12 CXIs zero-one loss, 99.24% AUC-ROC score, and lowest 1.33% error rate. Overall test results are superior to the existing Convolutional Neural Network(CNN). To the best of our knowledge, the observed results for nine-class classification are significantly superior to the state-of-the-art approaches employed for COVID-19 detection. Furthermore, the CXI that we used to assess our algorithm is one of the larger datasets for COVID detection with pulmonary diseases. Conclusion: The empirical findings of our suggested approach PulDi-COVID show that it outperforms previously developed methods. The suggested SSE method with PulDi-COVID can effectively fulfill the COVID-19 speedy detection needs with different lung diseases for physicians to minimize patient severity and mortality.
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In brief TED talk-style presentations on topics ranging from climate change, artificial intelligence (AI) and telemedicine, to the COVID-19 pandemic and emergence of corporate medicine, the radiologists shared their predictions of how these developments could change medical imaging technology and practice during the next five years. Given the expansion of telemedicine, the growth of radiologic consultation, and the emergence of new and improved diagnostic modalities-such as hybrid PET-MRI, photon-counting CT, new nuclear medicine radiotracers, and theranostics-as well as minimally invasive imaging-guided procedures and implementation of AI, Dr Morrison predicted the role of radiologists eventually will go beyond simply supplying and interpreting the images. The Carol D and Henry P Pendergrass Professor, chair of the radiology and radiological science department, and a professor of biomedical engineering at Vanderbilt University Medical Center and School of Medicine in Nashville, Tennessee, addressed the imminent dangers of climate change and radiology's role in both contributing to and helping alleviate those dangers. [...]what was once meant to protect physicians will become a major threat to radiologists over the next several years, says Mark E Schweitzer, MD, vice president of health affairs at Wayne State University in Detroit, Michigan.
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In this work, applications of microfluidic devices in the field of biomedical engineering will be described, with special focus on point-of-care tools (POC). These are devices offering rapid and easy-to-read diagnostic assays of various diseases or physiological conditions (COVID-19, allergies, cardiovascular diseases, tumours, pregnancy, etc.) that often can be applied by non-medically trained persons. Microfluidic devices play an important role in the development of POC tools. Recent advances in novel fabrication methods and multi-technology approaches allow to overcome common limitations, such as high cost, complex fluidic controls and pumping systems, or requiring specialized labour equipment and well-trained operators. Hence, these devices can be made available for a broad range of patients that might not have sufficient medical facilities. The influence of point-of-care tools on society, medical practice and early diagnosis will be discussed. Copyright © 2022 The Authors.
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seeing high-tech medical devices from other nations and witnessing surgery to learn has become nearly unattainable. The pandemic of coronavirus disease 2019 (COVID-19) has created cross-border medical education challenging. Nevertheless, to cater to the increase in non-face-to-face education, instructional techniques entailing the 'metaverse' are being initiated in the medical field, since medical staff from all over the globe who frequented the UAE to acquire skills in medical technology and medical students who require to exercise have already had minimal prospects to collaborate closely with patients attributable to COVID-19. Employing video-conferencing technology like Zoom to provide effective medical education is similarly difficult. The research's goal is to learn the perception of students in the UAE towards the metaverse system (MV) used for medical training. The conceptual model includes The Technology Acceptance Model (TAM) elements and adoption aspects of perceived value. The research's conceptual model, which connects both personal-based traits and technological features, is what makes it novel. Additionally, the novel hybrid analysis approach will be applied in the present research to conduct machine learning (ML) driven structural equation modeling (SEM) evaluation. © 2022 IEEE.