Industry 4.0 and Digitalisation in Healthcare

Failures and fallacies of eHealth initiatives: Are we finally able to overcome the underlying theoretical and practical orthodoxies?

The growing and ubiquitous digitalization trends embodied in eHealth initiatives have led to the widespread adoption of digital solutions in the healthcare sector. These initiatives have been heralded as a potent transformative force aiming to improve healthcare delivery, enhance patient outcomes and increase the efficiency of healthcare systems. However, despite the significant potential and possibilities offered by eHealth initiatives, the article highlights the importance of critically examining their implications and cautions against the misconception that technology alone can solve complex public health concerns and healthcare challenges. It emphasizes the need to critically consider the sociocultural context, education and training, organizational and institutional aspects, regulatory frameworks, user involvement and other important factors when implementing eHealth initiatives. Disregarding these crucial elements can render eHealth initiatives inefficient or even counterproductive. In view of that, the article identifies failures and fallacies that can hinder the success of eHealth initiatives and highlights areas where they often fall short of meeting rising and unjustified expectations. To address these challenges, the article recommends a more realistic and evidence-based approach to planning and implementing eHealth initiatives. It calls for consistent research agendas, appropriate evaluation methodologies and strategic orientations within eHealth initiatives. By adopting this approach, eHealth initiatives can contribute to the achievement of societal goals and the realization of the key health priorities and development imperatives of healthcare systems on a global scale.

4D Printing: The Development of Responsive Materials Using 3D-Printing Technology

Additive manufacturing, widely known as 3D printing, has revolutionized the production of biomaterials. While conventional 3D-printed structures are perceived as static, 4D printing introduces the ability to fabricate materials capable of self-transforming their configuration or function over time in response to external stimuli such as temperature, light, or electric field. This transformative technology has garnered significant attention in the field of biomedical engineering due to its potential to address limitations associated with traditional therapies. Here, we delve into an in-depth review of 4D-printing systems, exploring their diverse biomedical applications and meticulously evaluating their advantages and disadvantages. We emphasize the novelty of this review paper by highlighting the latest advancements and emerging trends in 4D-printing technology, particularly in the context of biomedical applications.

3D printing for spine pathologies: a state-of-the-art review

Three-Dimensional Printing has advanced throughout the years in the field of biomedical science with applications, especially in spine surgeries. 3D printing has the ability of fabricating highly complex structures with ease and high dimensional accuracy. The complexity of the spine's architecture and the inherent dangers of spinal surgery bring the evaluation of 3D printed models into consideration. This article summarizes the benefits of 3D printing based models for application in spine pathology. 3D printing technique is extensively used for fabrication of anatomical models, surgical guides and patient specific implants (PSI). The 3D printing based anatomical models assist in preoperative planning and training of students. Furthermore, 3D printed models can be used for improved communication and understanding of patients about the spinal disorders. The use of 3D printed surgical guides help in the stabilization of the spine during surgery, improving post procedural outcomes. Improved surgical results can be achieved by using PSIs that are tailored for patient specific needs. Finally, this review discusses the limitations and potential future scope of 3D printing in spine pathologies. 3D printing is still in its infancy, and further research would provide better understanding of the technology's true potential in spinal procedures.

Opportunities and challenges of 5G network technology toward precision medicine

Moving away from traditional "one-size-fits-all" treatment to precision-based medicine has tremendously improved disease prognosis, accuracy of diagnosis, disease progression prediction, and targeted-treatment. The current cutting-edge of 5G network technology is enabling a growing trend in precision medicine to extend its utility and value to the smart healthcare system. The 5G network technology will bring together big data, artificial intelligence, and machine learning to provide essential levels of connectivity to enable a new health ecosystem toward precision medicine. In the 5G-enabled health ecosystem, its applications involve predictive and preventative measurements which enable advances in patient personalization. This review aims to discuss the opportunities, challenges, and prospects posed to 5G network technology in moving forward to deliver personalized treatments and patient-centric care via a precision medicine approach.

Current state of the art and future directions for implantable sensors in medical technology: Clinical needs and engineering challenges

Implantable sensors have revolutionized the way we monitor biophysical and biochemical parameters by enabling real-time closed-loop intervention or therapy. These technologies align with the new era of healthcare known as healthcare 5.0, which encompasses smart disease control and detection, virtual care, intelligent health management, smart monitoring, and decision-making. This review explores the diverse biomedical applications of implantable temperature, mechanical, electrophysiological, optical, and electrochemical sensors. We delve into the engineering principles that serve as the foundation for their development. We also address the challenges faced by researchers and designers in bridging the gap between implantable sensor research and their clinical adoption by emphasizing the importance of careful consideration of clinical requirements and engineering challenges. We highlight the need for future research to explore issues such as long-term performance, biocompatibility, and power sources, as well as the potential for implantable sensors to transform healthcare across multiple disciplines. It is evident that implantable sensors have immense potential in the field of medical technology. However, the gap between research and clinical adoption remains wide, and there are still major obstacles to overcome before they can become a widely adopted part of medical practice.

Technology Management as a Core Component of a Client-centric Prosthetic Orthotic Practice Model

Technological innovation has transformed how we communicate, work, and conduct business. Over the next decade how we experience health care both as health care professionals and as client-patients will also change significantly. This presents both an opportunity and a challenge to medical clinical professionals that are device-focused, including prosthetists orthotists, as they consider how best to adapt. Current prosthetic orthotic education and practice is heavily clinically weighted, with less emphasis being given to engineering and business skills. Yet all three are essential core elements of a successful, sustainable prosthetics orthotics practice. Furthermore, it is the latter two that will heavily influence the future face of prosthetics & orthotics. It is not certain how current prosthetic orthotic practitioners can best adapt in response. One solution, proposed in this editorial, could be by rebalancing their professional persona to equally weight the three essential core elements. The result, a Clinical Prosthetic Orthotic Technology Management Professional, would engage in a professional practice that is functionally grounded, uses a client-centric model and incorporate eight professional attributes: professional, advocate, scholar, leader, communicator, collaborator, assistive technology expert and business justification specialist.

Comprehensibility of Contraindications in German, UK and US Summaries of Product Characteristics/Prescribing Information—A Comparative Qualitative and Quantitative Analysis

Contraindications (CIs) in Summaries of Product Characteristics (SmPCs)/Prescribing Information (PI) that lack clarity may pose a risk to medication safety and increase the risk for adverse drug reactions. We assessed and compared SmPCs/PI from three major drug markets regarding comprehensibility from the prescriber perspective, as well as usability in clinical decision support systems. 158 drugs met the following inclusion criteria: marketed in Germany (DE), United Kingdom (UK) and United States (US) and belonged to the 100 most recently FDA approved and/or 100 most frequently prescribed drugs in either country. In the 474 (3 × 158) SmPCs/PI all expressions for absolute CIs were identified, divided into 3999 stand-alone terms and evaluated according to ‘clarity’ and ‘codability’. The average number of absolute CIs per drug differed drastically between the three markets (DE: 11.7, UK: 9.0, US: 4.6). Expressions were frequently unclear (DE: 27.2% (95% CI 25.2–29.2%), UK: 28.5% (26.2–30.9%), US: 22.6% (19.7–25.8%)). Moreover, 60.9% (58.6–63.1%), 63.6% (61.0–66.0%), and 64.7% (61.2–68.1%) of the expressions were not codable in DE, UK, and US, respectively. Taken together, in three major drug markets, statements regarding CIs in SmPCs/PI substantially differ in frequency and frequently lack clarity and codability which poses an unnecessary obstacle to medication safety.

Reconditioning by Welding of Prosthesis Obtained through Additive Manufacturing

Biocompatible titanium alloys are increasingly being used to make custom medical implants using additive manufacturing processes. This paper considered the welding reconditioning of a titanium-alloy customized additive manufactured hip implant with several manufacturing defects. The personalized implants are made starting from a Computer-Aided Design (CAD) model as a direct result from the medical imaging investigations of the areas of interest. Then the customized implant is fabricated using an additive manufacturing process (in this case Powder Bed Fusion—Direct Metal Laser Sintering—DMLS). The analysis of the chemical composition values as well as the values of the mechanical properties of the samples obtained via DMLS additive manufacturing process, revealed that such a manufacturing process can be successfully used to make customized surgical implants. The mechanical properties values of the DMLS samples are approximately equal to those specified by the manufacturer of the titanium powder used for sintering. On average, the tensile strength was found to be 24.75% higher, while yield strength 22.7% higher than the values provided in the standard for surgical implants applications. In case the additive manufacturing process produces products with defects one might want to try and recover the implant due to costs and time constraints. The Tungsten Inert Gas (TIG) welding reconditioning process with ERTi-5 Ti64 rod for welding titanium alloys with a content of 6% aluminum and 4% vanadium filler material was used to restore the geometric characteristics as well as the functional properties of a custom hip medical prosthesis. After welding depositing successive layers of materials, the surfaces of the prosthesis were machined to restore the functional properties according to the characteristics of the original 3D model. A 3D scan was used to compare the geometrical characteristics between the original part and reconditioned one. Deviations were less than 1 mm and were acceptable from the medical point of view.

A Systematic Review of the Research Development on the Application of Machine Learning for Concrete

Research on the applications of new techniques such as machine learning is advancing rapidly. Machine learning methods are being employed to predict the characteristics of various kinds of concrete such as conventional concrete, recycled aggregate concrete, geopolymer concrete, fiber-reinforced concrete, etc. In this study, a scientometric-based review on machine learning applications for concrete was performed in order to evaluate the crucial characteristics of the literature. Typical review studies are limited in their capacity to link divergent portions of the literature systematically and precisely. Knowledge mapping, co-citation, and co-occurrence are among the most challenging aspects of innovative studies. The Scopus database was chosen for searching for and retrieving the data required to achieve the study’s aims. During the data analysis, the relevant sources of publications, relevant keywords, productive writers based on publications and citations, top articles based on citations received, and regions actively engaged in research into machine learning applications for concrete were identified. The citation, bibliographic, abstract, keyword, funding, and other data from 1367 relevant documents were retrieved and analyzed using the VOSviewer software tool. The application of machine learning in the construction sector will be advantageous in terms of economy, time-saving, and reduced requirement for effort. This study can aid researchers in building joint endeavors and exchanging innovative ideas and methods, due to the statistical and graphical portrayal of participating authors and countries.

A Comparison of Machine Learning Techniques for the Quality Classification of Molded Products

The developments in the internet of things (IoT), artificial intelligence (AI), and cyber-physical systems (CPS) are paving the way to the implementation of smart factories in what is commonly recognized as the fourth industrial revolution. In the manufacturing sector, these technological advancements are making Industry 4.0 a reality, with data-driven methodologies based on machine learning (ML) that are capable of extracting knowledge from the data collected by sensors placed on production machines. This is particularly relevant in plastic injection molding, with the objective of monitoring the quality of molded products from the parameters of the production process. In this regard, the main contribution of this paper is the systematic comparison of ML techniques to predict the quality classes of plastic molded products, using real data collected during the production process. Specifically, we compare six different classifiers on the data coming from the production of plastic road lenses. To run the comparison, we collected a dataset composed of the process parameters of 1451 road lenses. On such samples, we tested a multi-class classification, providing a statistical analysis of the results as well as of the importance of the input features. Among the tested classifiers, the ensembles of decision trees, i.e., random forest and gradient-boosted trees (GBT), achieved 95% accuracy in predicting the quality classes of molded products, showing the viability of the use of ML-based techniques for this purpose. The collected dataset and the source code of the experiments are available in a public, open-access repository, making the presented research fully reproducible.

Diffusion of a Lifelog-Based Digital Healthcare Platform for Future Precision Medicine: Data Provision and Verification Study

We propose a method for data provision, validation, and service expansion for the spread of a lifelog-based digital healthcare platform. The platform is an operational cloud-based platform, implemented in 2020, that has launched a tool that can validate and de-identify personal information in a data acquisition system dedicated to a center. The data acquired by the platform can be processed into products of statistical analysis and artificial intelligence (AI)-based deep learning modules. Application programming interfaces (APIs) have been developed to open data and can be linked in a programmatic manner. As a standardized policy, a series of procedures were performed from data collection to external sharing. The proposed platform collected 321.42 GB of data for 146 types of data. The reliability and consistency of the data were evaluated by an information system audit institution, with a defects ratio of approximately 0.03%. We presented definitions and examples of APIs developed in 17 functional units for data opening. In addition, the suitability of the de-identification tool was confirmed by evaluating the reduced risk of re-identification using quasi-identifiers. We presented specific methods for data verification, personal information de-identification, and service provision to ensure the sustainability of future digital healthcare platforms for precision medicine. The platform can contribute to the diffusion of the platform by linking data with external organizations and research environments in safe zones based on data reliability.

Dynamic Scheduling Method for Job-Shop Manufacturing Systems by Deep Reinforcement Learning with Proximal Policy Optimization

With the rapid development of Industrial 4.0, the modern manufacturing system has been experiencing profoundly digital transformation. The development of new technologies helps to improve the efficiency of production and the quality of products. However, for the increasingly complex production systems, operational decision making encounters more challenges in terms of having sustainable manufacturing to satisfy customers and markets’ rapidly changing demands. Nowadays, rule-based heuristic approaches are widely used for scheduling management in production systems, which, however, significantly depends on the expert domain knowledge. In this way, the efficiency of decision making could not be guaranteed nor meet the dynamic scheduling requirement in the job-shop manufacturing environment. In this study, we propose using deep reinforcement learning (DRL) methods to tackle the dynamic scheduling problem in the job-shop manufacturing system with unexpected machine failure. The proximal policy optimization (PPO) algorithm was used in the DRL framework to accelerate the learning process and improve performance. The proposed method was testified within a real-world dynamic production environment, and it performs better compared with the state-of-the-art methods.

A Review of In-Service Coating Health Monitoring Technologies: Towards “Smart” Neural-Like Networks for Condition-Based Preventive Maintenance

In line with the recent industrial trends of hyperconnectivity, 5G technology deployment, the Internet of Things (IoT) and Industry 4.0, the ultimate goal of corrosion prevention is the invention of smart coatings that are able to assess their own condition, predict the onset of corrosion and alert users just before it happens. It is of particular interest to tackle corrosion that occurs in non-accessible areas where human inspectors or handheld devices are useless. To accomplish this, a variety of technologies that are embedded or could potentially be embedded into the coatings are being developed to monitor coating condition, which are based, for instance, on the evolution of electrochemical or mechanical properties over time. For these technologies to be fully embedded into the coatings and work remotely, solutions are needed for connectivity and power supply. A paradigm shift from routine prescheduled maintenance to condition-based preventive maintenance could then become a reality. In this work, the technologies that enable the in-service monitoring of organic anticorrosion coatings were compiled. Soon, some of them could be integrated into the sensing elements of autonomous, connected neural-like networks that are capable of remotely assessing the condition of the anticorrosion protection of future infrastructures.