A knowledge graph-driven framework for deploying AI-powered patient digital twins

Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts.

In an era where the resilient planning of cities is mainly done by a multitude of digital tools for thinking, predicting, building, and observing, the data used remains globally in a static condition[1], however the city and its environment are in a perpetual state of change and must include dynamic data for being as close to reality as possible[2].Coming from the industrial world, the emerging concept of the "Digital Twin" (DT) is positioned as a tool linking the 3D digital model and the implementation of continuous and dynamic information (from sensors among others), allowing to introduce the notion of piloting and maintenance of doubled physical objects[3]. Applied at different scales and functions the "DT" becomes a unique database allowing both the simulation of different climate risk scenarios and the real-time assessment of the existing environment[4].Based on the existing literature[5] and on experimental projects such as European or National "DT" projects designed for resilient planning such as LEAD, DUET or DIAMS[6], this communication will first focus on the analysis of different dynamic and static environmental indicators used (definitions and management of the data). In a second step, we will concentrate on the different actors of these "DT" by questioning their expectations and needs (visualizations and simulations of the different impacts due to climate change as well as the maintenance of real subjects). But also by analyzing their processes of use to understand the advantages and the limits noticed during their experimentations. Finally, we will ask ourselves how this tool could evolve thanks to the feedback from these tests.The aim is to evaluate the role that the "DT" can have on the dynamic and resilient planning of territories and conversely the impacts of programming on the use of this tool. Keywords: Digital Twin, dynamic planning, urban resiliency Références : [1] Ramalho, Cristina E., et Richard J. Hobbs. “Time for a Change: Dynamic Urban Ecology” Trends in Ecology & Evolution 27, no 3 (2012): 179‑88. https://doi.org/10.1016/j.tree.2011.10.008. [2] Chunyang He et al., “Alternative Future Analysis for Assessing the Potential Impact of Climate Change on Urban Landscape Dynamics ”, Science of The Total Environment 532 (1 novembre 2015): 48‑60, https://doi.org/10.1016/j.scitotenv.2015.05.103. [3] A. Fuller, Z. Fan, C. Day and C. Barlow, "Digital Twin: Enabling Technologies, Challenges and Open Research," in IEEE Access, vol. 8, pp. 108952-108971, (2020), doi: 10.1109/ACCESS.2020.2998358. [4] Guillaud, M., & Chéreau, M. (2022). "3. Des outils techniques accessibles et des données ouvertes ". In Inventer les villes durables (p. 119‑133). Dunod. https://www.cairn.info/inventer-les-villes-durables--9782100834204-p-119.htm [5] Shahat, Ehab, Chang T. Hyun, et Chunho Yeom. « City Digital Twin Potentials: A Review and Research Agenda ». Sustainability 13, no 6 (janvier 2021): 3386. https://doi.org/10.3390/su13063386. [6]    Objectives Lead Project. (2023). https://www.leadproject.eu/objectives/  About digital urban european twins. (s. d.). DUET. https://www.digitalurbantwins.com/digitaltwindemo  Planification urbaine pour les collectivités pilotes DIAMS. (s. d.). https://www.airdiams.eu/planification-urbaine-pour-les-collectivites-pilotes

Biopharma Is Going Digital … Bit by Bit

The article focuses on the project to develop a supply chain management information system using a digital twin for the maritime transport chain. Digital twins reflect real objects and processes in a virtual environment, which enables analysis, modelling and optimisation of supply chain operations. The author describes the main stages of the project, including requirements definition, system architecture development, implementation and testing. Particular attention is paid to the benefits of using digital twins in supply chain management, such as increased efficiency, reduced risk and improved forecasting. The results of the study show the potential of digital twins to optimise supply chain management and increase the competitiveness of companies. The article presents an approach to building a new model of interaction and systematic optimisation of business processes based on digital technologies at all stages of supply chain formation, which ensures the creation of a digital twin for the maritime transport chain. In order to improve supply chains, a digital twin has been proposed, which opens up great prospects for the management of logistics processes and the exchange of information between supply chain participants. This paper presents a framework for an integral Digital Supply Chain Twin (DSCT) capable of covering the entire transport chain. The proposed information system based on the use of the DSCT provides a number of improvements throughout the supply chain, as well as the ability to model and evaluate different scenarios for the supply chain. This creates opportunities to reduce the cost of organising and coordinating transport, improve the quality of transport and logistics services and make them more reliable by identifying potential problems at an early stage. The article analyses the main information technologies that facilitate the use of DSCT as a useful and reliable tool.

The aircrafts were top of the places that disrupted the seamless connectivity requirement of 5G and beyond. The Aeronautical Ad-hoc Networks (AANETs) take the attention of both industry and academia to satisfy this connectivity requirement with the low cost, easy deployment, and continuous coverage features. On the other hand, the ultra-dynamic characteristics of AANET with unstructured topology make its environment hard-to-follow. Here, Artificial Intelligence (AI)-based methodologies have an essential role in handling the management complexity of this hard-to-follow environment. However, these methodologies increase the computational complexity of aircraft due to the continuous update, convergence time, and scalability issues. At that point, we propose the utilization of the Digital Twin (DT) technology to handle the management complexity of AANET while solving the main issues of AI-based methodologies on it. The DT can virtually replicate the physical AANET components through closed-loop feedback in real-time. Therefore, this work introduces the utilization of DT technology for the AANET orchestration and, accordingly, proposes a DT-enabled AANET (DT-AANET) topology management framework. This framework consists of the Physical AANET Twin and Controller, including Digital AANET Twin with Operational Module. Here, the Digital AANET Twin virtually represents the physical environment while the operational module executes the AI-based computations on them through unsupervised learning-based training or supervised learning-based prediction. Finally, we present a case study based on Learning Vector Quantization (LVQ) to show the usability of the proposed framework and support this through evaluation results.

Aiming at the increasingly complex management and dispatching problems of power system, this paper combines Beidou precise space-time technology with digital twin system theory, and puts forward a method of constructing intelligent management and dispatching platform of power grid. The method combines Beidou and IoT (Internet of Things) sensors to form a digital twin management and scheduling platform which can perform state prediction, fault diagnosis, self-healing and other functions. Firstly, this paper analyzes the overall architecture of Beidou digital power twin system, then discusses the construction method of digital twin in power system from the aspects of digital twin modeling theory, data acquisition intelligent terminal based on Beidou, operation mode of digital twin platform, etc. Finally, the application test analysis is carried out. The research results show that the method proposed in this paper has reference value for improving the management and dispatching capability of smart grid.

Digital twins appeared under four different names on the Gartner’s hype cycle of emerging technologies—as digital twins for business (2018), digital twins of government (2019), digital twins of a human (2021), and digital twin of a citizen (2022). Digital twins of government come closest to exemplifying digital twins for urban governance, however it doesn’t mean that the public sector is the technology’s sole user or beneficiary. Policymakers are just one of several urban stakeholders that leverage digital twins to achieve their objectives. Architects are using digital twins to create optimal designs to comply with planning regulations. Property developers take advantage of the model-based submission process to expedite an approval by a building authority. Construction professionals see digital twins as an opportunity to better manage on-site operations and de-risk digging. For utility companies, digital twins provide a means to prepare for and mitigate the impact of emergencies when they happen, such as sewage spills or flooding. The pool of use cases has grown substantially over the years. Even so, with an impressive body of knowledge accumulated to date, we barely scratch the surface of what is possible with digital twins. By incorporating a broader spectrum of elements that shape urban dynamics, digital twins have the potential to represent urban reality more holistically than is currently the case. Once more dimensions start getting added to complement a physical city, it may be more appropriate to talk not of digital twins but of digital multiples, such as triplets (with social dimension included), quadruplets (plus cultural), or quintuplets (plus political). It’s this enhanced representational capacity combined with the ability to change local reality through a two-way synchronisation with the physical world that separates digital twins from other e-governance tools like Geospatial Information Systems (GIS) or Building Information Models (BIM). That said, the road ahead is far from smooth as there are a number of challenges and challengers to the concept. The paper discusses them and provides a critical reflection on how digital twins can evolve to remain relevant in the future.

The agricultural domain is experiencing an increasing use of digital twin technologies in greenhouse horticulture for the betterment of monitoring production. Research in this domain has started leading towards more advanced ways of visually interacting with these digital twins, and this could be by means of immersive technologies (such as virtual reality) to better allow farmers to feel a sense of presence when exploring these digital copies. Yet there are many remaining challenges for the technology’s integration and more studies are needed into user interaction; specifically regarding simulation sickness to cater for comfort during prolonged and regular use. Thus, this article documents the survey of 30 participants by means of the Simulator Sickness Questionnaire (before and after interaction) when using an immersive digital greenhouse twin environment. Findings indicate that users who experience simulation sickness tend to provide a lower evaluation score of the digital twin and their prior experience of gaming (on varied devices) affects the overall evaluation of the digital environment. Further, the level of playability and realism (referred to as convenience), statistically affects the end users’ level of sickness. For tracking crop growth by means of digital greenhouse twins, where prolonged user interaction may take place in a virtual environment, these are notable considerations for digital twin developers.

<b><sc>Abstract.</sc></b> Cooling tomatoes after harvesting and keeping them refrigerated during their transportation is a key step toward preserving the quality attributes of tomatoes. This is a challenging task to accomplish in Nigeria, mainly because shipments of tomatoes from the tomato-producing states in northern Nigeria to the non-producing states in southern Nigeria were predominately not transported in a refrigerated ambient condition. To minimize postharvest losses of tomatoes during transportation, quantification of changes in quality would be extremely valuable. For this purpose, a digital tomato twin was developed based on mechanistic physics-based modeling. This digital twin simulated the actual thermal profile of tomatoes during transportation, based on sensed ambient environmental conditions. The impacts of the adoption of a low-cost, simple plastic packaging container compared with the traditional container (raffia baskets) on tomato's quality evolution during transportation were analyzed in-silico using the developed digital twin. Generally, the values of colour parameter (a*), firmness (N), and lycopene content (LC, mg/100ml) predicted by the digital twin model for tomatoes transported in raffia baskets were all higher than prediction for plastic container. For example, in shipment #2 the predicted colour parameter (a*) was 46.9 % more than the value predicted for the tomatoes in the plastic container at the end of the transportation. Similarly, lycopene content (LC) increased from 1.14 mg/100ml to 2.58 mg/100ml and 1.80 mg/100ml for raffia baskets and plastic container and firmness (N) decreased by 89% and 58 % in the raffia basket and plastic container respectively.

Digital twins (DTs) have been found useful in manufacturing, construction, and maintenance. Adapting DTs to serve cities, the question arises of what an urban digital twin should contain and how it should be orchestrated to serve a city’s dynamical ecosystem, along with how to enhance the efficiency of the city. We are aligning with the commonplace idea that the main advantage of using DTs is economical as, for example, DTs can improve the planning of activities thus saving money and time. But how can they be useful for a city? Instead of looking at the DTs as solutions in search of problems to be solved, we start from city needs. Our approach is two-fold. We start by briefly reviewing existing possibilities for meeting some specific needs, but keep the focus on identifying and attempting to close the gap between the needs arising from everyday city functions and the latest DT techniques useful for meeting those needs. DTs are technically different and serve different applications, yet they share a common identity and name, as well as several technical similarities. Adopting computer science terminology, we see a back-end city DT as the container of all information, while any single front-end, visualized or used either by humans or robots, offers a limited but meaningful representation of the DT for a specific application. Alas, there are multiple open questions regarding the realization and benefits of such back-end DT. Nevertheless, we discuss how the back-end DT (or any specific DT) could be updated autonomously from sensor data using artificial intelligence techniques, and how the front-ends could be used for large benefits to the entire city ecosystem. • There is a call to better match DT technology to meet overall city needs. • City DTs differ from DTs used in manufacturing, construction, and maintenance. • Differences include both technical (BIM-GIS) and human factor-induced complexities. • Novel AI methods could serve in automated updating of city DTs from sensor data. • Human factors and inclusion of third parties need to be considered for city DTs.

In modern medicine, novel technologies play a key role in improving the diagnosis, treatment, and management of patient health. One of the most promising innovations is Digital Twin technology, which enables the creation of virtual models of real-world objects, including human organs. This technology opens new possibilities for personalised medicine, disease prediction, and the optimisation of medical processes. Digital Twins are already actively used in various industries, such as manufacturing, aviation, and urban planning, but their potential in medicine is only beginning to unfold. They allow for creating detailed models of an individual patient’s body, contributing to more accurate diagnostics, simulation of treatment responses, and enhanced efficiency of medical interventions. This article explores the key aspects of Digital Twin medical applications, their advantages, challenges, and development prospects. Special attention is given to the technical foundations of this technology, its use in personalised medicine, health monitoring, disease prediction, and the optimisation of healthcare facilities. For the practical part, the study shows how to create a Digital Twin of a heart based on real patient data. Additionally, the study investigates a model of the heart’s Digital Twin, demonstrates its use, and illustrates how to replicate heart functionality using the Digital Twin. The study describes how a part of a Digital Twin was created using the suggested architecture and outlines how to use and extend this functionality. More than that, the study shows how to integrate artificial intelligence into the Digital Twin architecture and how artificial intelligence algorithms help create Digital Twins. The research shows how to build the Digital Twin using machine learning and artificial intelligence technologies, and how to choose the mathematical model for the Digital Twin. To conclude, the study defines further work, problems, and difficulties in creating the Digital Twin of the heart. The study shows the perspectives on using Digital Twins, their practicality, and real-life cases where Digital Twins can save lives. Also, the study defines the next steps for the Digital Twin creation, testing, implementation, and usage in the real-life healthcare industry.

Digital Twin was introduced over a decade ago, as an innovative all-encompassing tool, with perceived benefits including real-time monitoring, simulation, optimisation and accurate forecasting. However, the theoretical framework and practical implementations of digital twin (DT) are yet to fully achieve this vision at scale. Although an increasing number of successful implementations exist in research and industrial works, sufficient implementation details are not publicly available, making it difficult to fully assess their components and effectiveness, to draw comparisons, identify successful solutions, share lessons, and thus to jointly advance and benefit from the DT methodology. This work first presents a review of relevant DT research and industrial works, focusing on the key DT features, current approaches in different domains, and successful DT implementations, to infer the key DT components and properties, and to identify current limitations and reasons behind the delay in the widespread implementation and adoption of digital twin. This work identifies that the major reasons for this delay are: the fact the DT is still a fast evolving concept; the lack of a universal DT reference framework, e.g. DT standards are scarce and still evolving; problem- and domain-dependence; security concerns over shared data; lack of DT performance metrics; and reliance of digital twin on other fast-evolving technologies. Advancements in machine learning, Internet of Things (IoT) and big data have led to significant improvements in DT features such as real-time monitoring and accurate forecasting. Despite this progress and individual company-based efforts, certain research and implementation gaps exist in the field, which have so far prevented the widespread adoption of the DT concept and technology; these gaps are also discussed in this work. Based on reviews of past work and the identified gaps, this work then defines a conceptualisation of DT which includes its components and properties; these also validate the uniqueness of DT as a concept, when compared to similar concepts such as simulation, autonomous systems and optimisation. Real-life case studies are used to showcase the application of the conceptualisation. This work discusses the state-of-the-art in DT, addresses relevant and timely DT questions, and identifies novel research questions, thus contributing to a better understanding of the DT paradigm and advancing the theory and practice of DT and its allied technologies.

Digital twin technology has been increasingly applied in healthcare and patient well-being in recent years. This paper provides an overview of the current methods and applications of digital twins in the healthcare field. One such application is digital twins in precision healthcare, where digital twins are used to create patient-specific models to assist in diagnosis and treatment planning. Digital twins are also used in hospital/clinic management, where they help to optimize resource allocation and workflow processes. In response to the COVID-19 pandemic, digital twins have been utilized to detect outbreaks and predict disease spread. In addition, digital twins have been applied in bio-manufacturing and pharmaceutical industry to improve manufacturing processes. Another application area is machine learning and modeling, where digital twins are used in machine learning, data generation, and system modeling for applications in healthcare and disease prediction. Security and ethical issues related to digital twins are also discussed in this paper, as privacy concerns and data protection remain important considerations in the application of digital twin technology in healthcare. Finally, the paper concludes by discussing the future challenges and directions of future work in this field. These include the need to develop more accurate and sophisticated digital twin models, addressing interoperability and integration issues, and further exploring the potential of digital twin technology in emerging areas such as telemedicine and personalized medicine.

Digital twin (DT) has attracted a lot of attention from both industry and academia since it was proposed over a decade ago. A DT can be viewed as a virtual implementation of a real physical system (PS) and used as a representation of the PS for various applications. Despite the great potential of DTs in various fields, implementing DTs to obtain the desired functionality is not always straightforward. Specifically, accurate real-time synchronization between the features at a PS and its DT is essential for the DT to represent the PS. In this case, appropriate networking support is a key component to enable future DT development and applications. Currently, the research on DTs from a networking standpoint is still at an early stage, and only limited work has been done on DT implementation in practical systems. To fill this gap, this article investigates networking-related issues for DTs. Based on the existing literature, a feature-based method is provided for describing the desired properties and quality of DTs from the networking perspective. A stage-based implementation framework is presented for creating large-scale DTs for complex PSs by considering various networking constraints. Networking-related challenging issues and open research topics are discussed at the end.

Digital Twins (DTs) are gaining interest among urban planners and city authorities, but a notable gap exists in fully understanding and effectively applying them to complex and multifaceted transport planning processes. This review is an in-depth exploration of the promising concept of DTs and its implications for transport planning—its current applications and conceptualisations within the domain, focusing on their distinction from traditional transport models and identifying critical areas for further DT development. The review was conducted per PRISMA guidelines to identify 136 relevant studies from 2000 to 2024. Over three-quarters of the articles were published after 2021, indicating a growing focus on digital twins in transport planning. A classical five-component definition of DT, consisting of physical, digital, data model, service and connection components, was adapted and used to synthesise key components of the DTs presented in the studies. Simulations were the most common “data model,” and 31% of studies do not present insight into what “services” could be derived from proposed DTs, indicating a top-down approach to DT development. A prevalent misapplication of the DT concept was observed where most studies conflated basic digital models or shadows as DTs without fully meeting the criteria of real-time data integration or bi-directional interaction with physical systems. Interestingly, mature DTs with real-time data and bidirectional interaction were associated with smaller geographical systems and shorter time-range predictions. This suggested that maturity in transport planning DTs depends not only on technological capabilities but also on the spatiotemporal dimension of the system. The review concludes by suggesting directions towards a revised DT maturity, highlighting the level of maturity needed for “usable DTs”, and advocating the need for a bottom-up approach to DT development rather than the top-down one currently adopted by most studies.