Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

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Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

Integrated digital twin and blockchain framework to support accountable information sharing in construction projects

An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adjustment to the patient's characteristics, is still far from being part of the everyday care of pwMS. The development of digital twins could decisively advance the necessary implementation of an individualized innovative management of MS. Through artificial intelligence-based analysis of several disease parameters - including clinical and para-clinical outcomes, multi-omics, biomarkers, patient-related data, information about the patient's life circumstances and plans, and medical procedures - a digital twin paired to the patient's characteristic can be created, enabling healthcare professionals to handle large amounts of patient data. This can contribute to a more personalized and effective care by integrating data from multiple sources in a standardized manner, implementing individualized clinical pathways, supporting physician-patient communication and facilitating a shared decision-making. With a clear display of pre-analyzed patient data on a dashboard, patient participation and individualized clinical decisions as well as the prediction of disease progression and treatment simulation could become possible. In this review, we focus on the advantages, challenges and practical aspects of digital twins in the management of MS. We discuss the use of digital twins for MS as a revolutionary tool to improve diagnosis, monitoring and therapy refining patients' well-being, saving economic costs, and enabling prevention of disease progression. Digital twins will help make precision medicine and patient-centered care a reality in everyday life.

/pdf/digital-twins-for-multiple-sclerosis-1v0rc1lo3a.pdf

Digital Twins for Multiple Sclerosis.

A digital twin can be defined as an adaptive model of a complex physical system. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

Digital Twin: Values, Challenges and Enablers

Vulnerability retrospection of security solutions for software-defined Cyber–Physical System against DDoS and IoT-DDoS attacks

Cyber-Physical Systems and Digital Twins are commonly used today in the industrial sector, and the healthcare sector is keen to implement these technological solutions to enhance their capabilities and offer better services for patient care provision. In fact, the adoption of Wireless Body Area Networks (WBAN) based on IoT along with cloud computing systems has led to the development of new methodologies to monitor and treat patients. However, the adoption of the new technologies comes with several challenges in terms of performance and security. Considering that, WBAN can be wearable or implanted under the skin, and the overall concept leads to several cybersecurity challenges that would require deeper investigation. This chapter presents an analysis of the impact that WBAN has on health care. It also provides some definitions of Medical Cyber-Physical Systems (MCPSs) and Digital Twins along with technological enablers such as cloud and IoT.

Health Care in the Cyberspace: Medical Cyber-Physical System and Digital Twin Challenges

Computer storage and cloud computing has become more powerful with multiple algorithms running complex data analysis looking at intelligence trends, user behaviour, profiling and ways to make use of these outputs. Added with the artificial intelligence (AI) interaction has meant a new and dynamic method to create models forging analysis to be more clinical, proficient and continually seeking more improvement with the self-learning and intelligent programming of machine learning (ML). In the healthcare sector there is deep interest in collecting, curating the data and making the best use of silo’d data through methods such as blockchain. This can then lead to a multitude of innovations such as precision based medicine, targeting individual variability in genes, their environment, etc. It also means that big data analytics in healthcare is evolving into providing these insights from very large data sets and improving outcomes while reducing costs and inefficiencies. However, there also are some ethical impacts in the process of Digital Twins which can lead to segmentation and discrimination. Or perhaps the data that is automatically collected from healthcare sensors in IoMT and what type of governance are they scrutinized to. It is clear that data is the most important asset of not just an organisation but also to the individual and why the General Data Protection Regulation (GDPR) has taken an important stance in data protection by design and default, that all organisations needs to follow. This chapter aims to highlight some of the concerns.

Jaime Ibarra Jimenez

Papers

Health Care in the Cyberspace: Medical Cyber-Physical System and Digital Twin Challenges

The Transparency of Big Data, Data Harvesting and Digital Twins