Littérature scientifique sur le sujet « Software-Driven medical technologies »

Recent discovered technologies have exposed many new theories and possibilities to improve our standard of living. Medical assistance has been a major research topic in the past, many efforts were put in to simplify the process of following treatment prescriptions. This paper summarizes the work done in developing LoRa driven medical adherence system in order to improve medicine adherence for elderlies. The designed system is composed of two sections; embedded hardware device for the use of patients at home and Web application to manage all patients along with their medicines and keep track of their medicine intake history. LoRa wireless communication technology is used for connecting all embedded devices with a central gateway that manages the network. Hardware and software tests have been conducted and showed great performance in terms of LoRa network range and latency. In short, the proposed system shows promising method of improving medicine adherence.

Medical devices play a crucial role in human health. These are instruments, machines or even software programs used to diagnose, treat, monitor or prevent health conditions. They are designed to help improve patients’ quality of life and range from simple items, such as thermometers, to more advanced technologies, such as pacemakers. In order to guarantee the safety and efficacy of medical devices intended for use on patients, the establishment of appropriate regulatory frameworks is crucial to ascertain whether devices function as intended, comply with safety standards and offer benefits that outweigh the associated risks. Depending on the country, different regulatory agencies are responsible for the evaluation of these products. The regulatory landscape for medical devices varies significantly across major markets, including the European Union, the United States of America and Japan, reflecting diverse approaches aimed at ensuring the safety and efficacy of medical technologies. However, these regulatory differences can contribute to a “medical device lag,” where disparities in approval processes and market entry timelines driven by strict regulatory requirements, increasing device complexity and the lack of global harmonization, result in delays in accessing innovative technologies. These delays impact patient access to cutting-edge medical devices and competitiveness in the market. This review aims to address the regulatory framework of medical devices and the approval requirements by the European Commission (EC), the Food and Drug Administration (FDA) and Pharmaceuticals and Medical Device Agency (PMDA).

The escalating development of digital technologies over the last several decades has given rise to an accompanying surge in data analysis.The software based smart applications increase the importance of behaviour analysis. Software testing of expert systems such as electronic health records, health information system and software as a medical device (SaMD) refers to detect the difference between expected behaviour and actual outcome during healthcare expert systems development. Test cases are the core source of effective software testing. Test cases generation for expert systems are discover challenges of identifying the expected behaviour of the system, where the decision logic is obtained via a data –driven paradigm. In the traditional system software object oriented expected behaviour, provide the clear test cases does not change the flow such as in the healthcare system. Intelligent software test case generation approach required for smart health systems. In this research contributes key performance indicators from massive data sets, node –link diagrams from decision trees, test cases from adjacency matrix are elaborated. The experimental results of healthcare expert systems provide empirically evidence that topological data analysis are compact contribution for software requirement validation.

Abstract Predictions about the future of laboratory medicine have had a mixed success, and in some instances they have been overambitious and incorrectly assessed the future impact of emerging technologies. Current predictions suggest a more highly automated and connected future for diagnostic testing. The central laboratory of the future may be dominated by more robotics and more connectivity in order to take advantage of the benefits of the Internet of Things and artificial intelligence (AI)-based systems (e.g. decision support software and imaging analytics). For point-of-care testing, mobile health (mHealth) may be in the ascendancy driven by healthcare initiatives from technology companies such as Amazon, Apple, Facebook, Google, IBM, Microsoft and Uber.

Advanced robotics does not always have to be associated with Industry 4.0, but can also be applied, for example, in the Smart Hospital concept. Developments in this field have been driven by the coronavirus disease (COVID-19), and any improvement in the work of medical staff is welcome. In this paper, an experimental robotic platform was designed and implemented whose main function is the swabbing samples from the nasal vestibule. The robotic platform represents a complete integration of software and hardware, where the operator has access to a web-based application and can control a number of functions. The increased safety and collaborative approach cannot be overlooked. The result of this work is a functional prototype of the robotic platform that can be further extended, for example, by using alternative technologies, extending patient safety, or clinical tests and studies. Code is available at https://github.com/Steigner/Robo_Medicinae_I

This paper discusses experiences and architectural concepts developed and tested aimed at acquisition and processing of biomedical data in large scale system for elderly (patients) monitoring. Major assumptions for the research included utilisation of wearable and mobile technologies, supporting maximum number of inertial and biomedical data to support decision algorithms. Although medical diagnostics and decision algorithms have not been the main aim of the research, this preliminary phase was crucial to test capabilities of existing off-the-shelf technologies and functional responsibilities of system’s logic components. Architecture variants contained several schemes for data processing moving the responsibility for signal feature extraction, data classification and pattern recognition from wearable to mobile up to server facilities. Analysis of transmission and processing delays provided architecture variants pros and cons but most of all knowledge about applicability in medical, military and fitness domains. To evaluate and construct architecture, a set of alternative technology stacks and quantitative measures has been defined. The major architecture characteristics (high availability, scalability, reliability) have been defined imposing asynchronous processing of sensor data, efficient data representation, iterative reporting, event-driven processing, restricting pulling operations. Sensor data processing persist the original data on handhelds but is mainly aimed at extracting chosen set of signal features calculated for specific time windows – varying for analysed signals and the sensor data acquisition rates. Long term monitoring of patients requires also development of mechanisms, which probe the patient and in case of detecting anomalies or drastic characteristic changes tune the data acquisition process. This paper describes experiences connected with design of scalable decision support tool and evaluation techniques for architectural concepts implemented within the mobile and server software.

In developing countries like Nigeria, malaria and typhoid fever are major health challenges in society today. The symptoms vary and can lead to other illnesses in the body which include prolonged fever, fatigue, nausea, headaches, and the risk of contracting infection occurring concurrently if not properly diagnosed and treated. There is a strong need for cost-effective technologies to manage disease processes and reduce morbidity and mortality in developing countries. Some of the challenging issues confronting healthcare are lack of proper processing of data and delay in the dissemination of health information, which often causes delays in the provision of results and poor quality of service delivery. This paper addressed the weaknesses of the existing system through the development of an Artificial Intelligence (AI) driven extended diagnostic system (EDS). The dataset was obtained from patients’ historical records from the Lagos University Teaching Hospital (LUTH) and contained two-hundred and fifty (250) records with five (5) attributes such as risk level, gender, symptom 1, symptom 2, and ailment type. The malaria and typhoid dataset was pre-processed and cleansed to remove unwanted data and information. The EDS was developed using the Naive Bayes technique and implemented using software development tools. The performance of the system was evaluated using the following known metrics: accuracies of true positive (TP), true negative (TN), false positive (FP), and false negative (FN). The performance of the EDS was substantially significant for both malaria and typhoid fevers.

The digitalization of healthcare records in Bangladesh presents both opportunities and challenges, particularly concerning the security and protection of sensitive patient information. As electronic health records (EHRs) become increasingly prevalent, the threat of cyberattacks targeting medical data escalates, necessitating innovative solutions to fortify the country's healthcare cybersecurity infrastructure. This paper investigates the efficacy of self-learning artificial intelligence (AI) systems in safeguarding Bangladeshi medical records against cyber threats. The traditional methods of securing medical records, such as firewalls and antivirus software, are proving inadequate against the evolving tactics of cybercriminals. Bangladesh faces unique challenges in this regard, including limited resources, lack of cybersecurity awareness among healthcare professionals, technological fragmentation, and an increasingly sophisticated threat landscape. To address these challenges, there is a growing imperative to explore novel approaches that can adapt and evolve in real-time to counter emerging cyber threats. Self-learning AI systems represent a promising frontier in healthcare cybersecurity. By leveraging advanced machine learning algorithms, these systems can analyze vast amounts of data to detect patterns indicative of cyber threats. Unlike static security measures, self-learning AI continuously learns from new information and adjust their defense strategies, accordingly, enabling them to stay ahead of evolving threats. Key functionalities of self-learning AI include anomaly detection, threat prediction, and adaptive defense mechanisms, all of which are essential for safeguarding medical records in Bangladesh's healthcare landscape. The implications of integrating self-learning AI into Bangladesh's healthcare cybersecurity framework are significant. Not only can these technologies enhance the detection and prevention of cyber threats, but they can also alleviate resource constraints and technical challenges faced by healthcare organizations. However, successful implementation requires comprehensive training, adherence to data privacy regulations, and ongoing monitoring to ensure the effectiveness and reliability of AI-driven security measures. The protection of medical records is paramount as Bangladesh continues its digital transformation in healthcare. Self-learning AI offers a dynamic and proactive approach to cybersecurity, empowering healthcare organizations to mitigate risks and preserve patient privacy in an increasingly digitized landscape. Embracing these innovative technologies is crucial for building a resilient healthcare ecosystem that prioritizes data security and patient trust.

Advancements in patient care and diagnostic accuracy have been driven by the emergence of embedded systems, which has had a dramatic influence on the design and implementation of high-performance medical applications. This abstract explores the fundamental features of embedded system design that are specifically designed for medical applications. Particular attention is paid to the optimization of performance, the dependability of the system, and its integration within demanding healthcare contexts. When it comes to real-time processing, precision, and safety, embedded systems in medical devices are required to fulfill several severe standards. For the purpose of improving the performance of embedded systems that are used in high-performance medical applications, this article provides an overview of the important design considerations and tactics that are involved. Embedded systems are specialized computer systems that are situated inside a larger device and are responsible for performing certain duties. When it comes to the realm of medicine, these systems are an essential component of many technologies, including imaging machines, patient monitoring systems, and diagnostic instruments. The necessity for high dependability and real-time processing poses a unique set of issues when it comes to the design of embedded systems for use in medical applications. To guarantee the operation of the system, it is necessary to address concerns such as the amount of power used, the integrity of the data, and the capability to function in a variety of environments. Real-time processing is one of the most important characteristics to take into account when designing embedded systems for use in medical applications. In order to give rapid diagnosis and treatments, medical devices often need to respond immediately to sensor inputs or patient data. In order to achieve real-time processing, it is necessary to first optimize both the hardware and the software in order to reduce latency and guarantee correct data processing. It is very necessary to make use of sophisticated methods such as parallel processing and efficient algorithms in order to fulfill these criteria.

Driven by technological advances in various fields (AI, 5G, VR, IoT, etc.) together with the emergence of digital twins technologies (HDT, HAL, BIM, etc.), the Metaverse has attracted growing attention from scientific and industrial communities. This interest is due to its potential impact on people lives in different sectors such as education or medicine. Specific solutions can also increase inclusiveness of people with disabilities that are an impediment to a fulfilled life. However, security and privacy concerns remain the main obstacles to its development. Particularly, the data involved in the Metaverse can be comprehensive with enough granularity to build a highly detailed digital copy of the real world, including a Human Digital Twin of a person. Existing security countermeasures are largely ineffective and lack adaptability to the specific needs of Metaverse applications. Furthermore, the virtual worlds in a large-scale Metaverse can be highly varied in terms of hardware implementation, communication interfaces, and software, which poses huge interoperability difficulties. This paper aims to analyse the risks and opportunities associated with adopting digital replicas of humans (HDTs) within the Metaverse and the challenges related to managing digital identities in this context. By examining the current technological landscape, we identify several open technological challenges that currently limit the adoption of HDTs and the Metaverse. Additionally, this paper explores a range of promising technologies and methodologies to assess their suitability within the Metaverse context. Finally, two example scenarios are presented in the Medical and Education fields.

De nombreux obstacles entravent l'émergence de nouvelles technologies médicales issues de la recherche. L'un des principaux défis du transfert de technologie réside dans le temps et les efforts nécessaires pour se conformer aux réglementations. Dans ce contexte, l'évaluation de la maturité des technologies médicales devient un aspect incontournable, mais représente un véritable défi.Les méthodes d'évaluation de la maturité technologique sont des approches utilisées pour évaluer le niveau de développement et de fiabilité d'une technologie spécifique. Dans le domaine des systèmes d'information, ces méthodes sont couramment utilisées pour évaluer la maturité des infrastructures technologiques, des logiciels et des processus informatiques. Elles permettent d'identifier les forces et les faiblesses d'une technologie, d'orienter les décisions d'investissement et d'optimiser les processus de développement en vue d'une utilisation efficace de ces technologies dans leur contexte d'utilisation finale.Cependant, l'utilisation des méthodes d'évaluation de la maturité existantes dans la phase de recherche n'est pas adaptée ni appropriée, étant donné le caractère contraignant qu'elles impliquent. L'objectif de cette thèse est de démontrer qu'il est possible de mettre en place une approche non contraignante pour évaluer et mesurer la maturité technologique dans la phase de recherche des technologies médicales pilotées par logiciel (SdMT, pour Software-driven Medical Technology).Pour atteindre cet objectif, nos travaux fournissent les bases nécessaires pour la construction d'un modèle de maturité. Ce modèle vise à permettre l'évaluation quantitative et objective du niveau de préparation d'une SdMT, à un moment donné, en vue de son potentiel de transfert. Ce transfert peut concerner des projets de recherche collaboratifs, des essais cliniques ou des processus soumis à des contraintes réglementaires. Le modèle proposé peut fournir des indications sur les actions à entreprendre pour accroître le niveau de maturité d'une SdMT.Les travaux réalisés dans le cadre de cette thèse proposent une base solide composée d'un modèle d'artefacts de la norme ISO/IEC 62304, d'un questionnaire d'évaluation appelé SMAQ (SdMT Maturity Assessment Questionnaire) et d'un score de maturité. La validité du SMAQ a été démontrée par le biais d'une enquête menée auprès d'experts du domaine, ce qui a permis d'obtenir une version consolidée du questionnaire.Sur la base des résultats obtenus, il a été possible de définir les premiers éléments d'un modèle de maturité, tels que les niveaux de maturité, la trajectoire vers la maturité la plus élevée et les questions d'évaluation de la maturité. Les dimensions ainsi que les sous-catégories du modèle de maturité restent à définir plus précisément dans le cadre de travaux futurs
Numerous obstacles hinder the emergence of new medical technologies stemming from research. One of the primary challenges in technology transfer is the time and effort required to comply with regulations. In this context, evaluating the maturity of medical technologies becomes essential, but it poses a significant challenge.Methods for assessing technological maturity are approaches used to evaluate the development and reliability level of specific technologies. In the field of information systems, these methods are commonly employed to assess the maturity of technological infrastructures, software, and IT processes. They identify the strengths and weaknesses of a technology, guide investment decisions, and optimize development processes for efficient utilization of these technologies in their intended contexts.However, using existing maturity assessment methods during the research phase is not suitable or feasible due to their restrictive nature. The objective of this thesis is to demonstrate a non-binding approach to implement and measure technological maturity during the research phase of software-driven medical technologies (SdMT).To achieve this goal, our work provides the necessary foundations for constructing a maturity model. The model aims to enable quantitative and objective evaluation of the readiness level of an SdMT at a given point in time, regarding its potential for transfer. This transfer could involve collaborative research projects, progressing towards clinical trials, or entering into a regulated process. The proposed model can provide insights into the necessary actions to enhance the maturity level.The work conducted in this thesis offers a solid foundation, including a model of artifacts based on ISO/IEC 62304 standard, an evaluation questionnaire called SMAQ (SdMT Maturity Assessment Questionnaire), and a maturity score. The validity of the SMAQ has been demonstrated through a survey conducted among domain experts, resulting in a consolidated version of the questionnaire.Based on the obtained results, it has been possible to define initial elements of a maturity model, such as maturity levels, the path towards the highest maturity level, and the maturity assessment questions. The dimensions and sub-categories of the maturity model will be further defined in future work

Dietary and exercise interventions are the mainstay of prevention, and they constitute important part in the treatment of type 2 diabetes (DM2) and its complications. Automated, continuous, individualized non-invasive measurement of pathological processes leading to DM2 and complications are needed in terms of self-explaining metrics for improved individualized lifestyle management. Our company, the Ori Diagnostic Instruments, LLC is using tools of Medical Cybernetics (MC) to monitor non-invasive indicators of insulin resistance, exercise capacity, and autonomic dysfunction. The MC approach utilizes mathematical process and measurement models which are connected to a wearable sensor system. This chapter has the purpose to show how already widely available information technologies like smart phones, cloud computing, and sensor devices of the fitness industry could be put together into an integrated cyber-physical system (ICPS) to support fitness goals like fighting cardiometabolic conditions including high insulin resistance and low level of cardiorespiratory fitness and help building resilience with improved physiological reserve capacity. We want to demonstrate also how ICPS can be not only used for fitness self-management but can be extended to become a platform of noninvasive monitoring devices and become a medical software to support person-centered, outcome driven treatments for DM2 and complications in primary care.

Cyber-Physical Systems (CPS) are being developed with the integration of computational capabilities and communication with physical systems. Recent technologies like WSN (Wireless Sensor Networks), Communication Networks, Cloud Computing, Big Data, and many more use CPS. Applications include Smart Grid, Healthcare, Transportation, and Smart Buildings. CPS has made a huge impact on increasing the efficiency of medicines and life span in many developed countries. This has drawn researchers and the government's interest in Medical Cyber-Physical Systems (MCPS), especially during Covid times. Many models were developed during the pandemic. One of them is a Machine Learning (ML) integrated X-ray device called Covilearn. In this chapter, an introduction to CPS in health care, its current trends, and a device called e-stocking has been discussed. It is being used to treat leg venous insufficiency. The motive of the application of CPS is to reduce power consumption for long-term usage. This system is based on a model-driven energy-aware approach. There are three approaches - Mechanical, Software, and Communication. False alarm is one of the major issues as it is a pressure on both patients and caretakers. A model aimed at resolving this problem is proposed and discussed here. Also, the existing challenges and prospective opportunities in the domain of CPS have been explored.

The continual development of technology represents a challenge when preparing engineering students for future employment. At the same time, the way students interact in everyday life is evolving: their extra-curricular life is filled with an enormous amount of stimulus, from online data to rich Web-based social interaction. This chapter provides an assessment of various learning technology-driven methods for enhancing both teaching and learning in the science and engineering disciplines. It describes the past, present, and future drivers for the implementation of hands-on teaching methods, incorporating industry standard software and hardware and the evolution of learning experiments into all-encompassing online environments that include socializing, learning, entertainment, and any other aspect of student life when studying science and engineering.