Rethinking Metabolic Imaging: From Static Snapshots to Metabolic Intelligence

Digital twin (DT) systems, which involve creating virtual replicas of physical objects or systems, have the potential to transform healthcare by offering personalised and predictive models that grant deeper insight into a patient's condition. This review explores current concepts in DT systems for musculoskeletal (MSK) applications through an overview of the key components, technologies, clinical uses, challenges, and future directions that define this rapidly growing field. DT systems leverage computational models such as multibody dynamics and finite element analysis to simulate the mechanical behaviour of MSK structures, while integration with wearable technologies allows real-time monitoring and feedback, facilitating preventive measures, and adaptive care strategies. Early applications of DT systems to MSK include optimising the monitoring of exercise and rehabilitation, analysing joint mechanics for personalised surgical techniques, and predicting post-operative outcomes. While still under development, these advancements promise to revolutionise MSK care by improving surgical planning, reducing complications, and personalising patient rehabilitation strategies. Integrating advanced machine learning algorithms can enhance the predictive abilities of DTs and provide a better understanding of disease processes through explainable artificial intelligence (AI). Despite their potential, DT systems face significant challenges. These include integrating multi-modal data, modelling ageing and damage, efficiently using computational resources and developing clinically accurate and impactful models. Addressing these challenges will require multidisciplinary collaboration. Furthermore, guaranteeing patient privacy and protection against bias is extremely important, as is navigating regulatory requirements for clinical adoption. DT systems present a significant opportunity to improve patient care, made possible by recent technological advancements in several fields, including wearable sensors, computational modelling of biological structures, and AI. As these technologies continue to mature and their integration is streamlined, DT systems may fast-track medical innovation, ushering in a new era of rapid improvement of treatment outcomes and broadening the scope of preventive medicine. Level of Evidence: Level V.

New technologies enable the creation of digital twin systems (DTS) combining continuous data collection from children’s home and artificial intelligence (AI)-based recommendations to adapt their care in real time. The objective was to assess whether children and adolescents with asthma would be ready to use such DTS. A mixed-method study was conducted with 104 asthma patients aged 8 to 17 years. The potential advantages and disadvantages associated with AI and the use of DTS were collected in semi-structured interviews. Children were then asked whether they would agree to use a DTS for the daily management of their asthma. The strength of their decision was assessed as well as the factors determining their choice. The main advantages of DTS identified by children were the possibility to be (i) supported in managing their asthma (ii) from home and (iii) in real time. Technical issues and the risk of loss of humanity were the main drawbacks reported. Half of the children (56%) were willing to use a DTS for the daily management of their asthma if it was as effective as current care, and up to 93% if it was more effective. Those with the best computer skills were more likely to choose the DTS, while those who placed a high value on the physician–patient relationship were less likely to do so. Conclusions: The majority of children were ready to use a DTS for the management of their asthma, particularly if it was more effective than current care. The results of this study support the development of DTS for childhood asthma and the evaluation of their effectiveness in clinical trials.What is Known:• New technologies enable the creation of digital twin systems (DTS) for children with asthma.• Acceptance of these DTSs by children with asthma is unknown.What is New:• Half of the children (56%) were willing to use a DTS for the daily management of their asthma if it was as effective as current care, and up to 93% if it was more effective.•Children identified the ability to be supported from home and in real time as the main benefits of DTS.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00431-022-04754-8.

This paper aims to establish a predictive model for hydro turbine failures by simulating rare real-world data using a digital twin system. Hydro turbines play a critical role in the renewable energy sector, but their unpredictability in terms of failures results in significant maintenance and operational costs. The traditional fault prediction method based on historical data is difficult to achieve more accurate and generalized modeling, because the data in the real world cannot meet the requirements of the machine learning theory for the same distribution of data and data balance, especially some rare events are difficult to collect in reality. Therefore, in this paper, it is proposed to enhance the robustness of hydro turbine failure prediction by simulating data from some rare situations through a digital twin system. By collecting and simulating rare data from actual hydroelectric turbines, we gain a better understanding of their operational mechanisms and fault patterns. We propose a digital twin system capable of replicating real-world operating conditions in a virtual environment, which serves as the foundation for data-driven fault prediction models. Through deep learning analysis of the simulated data, we can predict the likelihood of hydro turbine failures, thus improving maintenance strategies, reducing costs, and enhancing turbine reliability. Our research offers a promising approach to addressing rare data challenges using digital twin systems and holds broad application potential within the hydropower industry.

Artificial Intelligence (AI), Artificial General Intelligence (AGI), and other emerging technologies are significantly reshaping modern healthcare systems. Their integration across clinical, operational, and public health settings has already produced measurable improvements in diagnostic accuracy, treatment personalization, operational efficiency, and epidemic response. These technologies leverage vast amounts of data, advanced algorithms, and computational power to augment clinical decision-making, optimize workflows, and expand access to care. This manuscript explores the real-world applications of these technologies, drawing on recent literature and case studies to illustrate both their potential and limitations. Specific examples include AI-driven diagnostic imaging, predictive analytics for hospital management, and AI-based models for pandemic surveillance. It also addresses the growing use of AI in personalized medicine and the increasing incorporation of robotics, deep learning, natural language processing, edge computing, quantum computing, health information and learning technologies (HILT), digital twin systems, and neural networks in everyday clinical practice (Topol, 2019; Rajkomar et al., 2019; Esteva et al., 2017). The findings indicate that while AI and related innovations hold promise for revolutionizing care delivery, challenges related to algorithmic bias, data privacy, ethical governance, and regulatory oversight remain critical considerations. The disparity in access to these tools, particularly in low-resource settings, underscores the need for inclusive and equitable frameworks. A multi-stakeholder, ethical, and interdisciplinary approach is required to ensure these tools fulfill their transformative potential while safeguarding patient rights and promoting equitable healthcare outcomes worldwide. As the healthcare landscape evolves, the thoughtful integration of AI, AGI, and complementary technologies will be pivotal in achieving scalable, efficient, and patient-centered care delivery.

Intelligence is an abstract topic and Turing test, as an external behavior detection method, provides a scientific method to test representational artificial intelligence. In this work, we briefly discuss the categories of artificial intelligence in nature. Then, according to the characteristics of the process industry, we propose a new Turing test paradigm that is suitable for process operation practice from the perspective of behaviorism and pragmatism, called Industrial Turing Test. We designed a digital twin system of air separation process using virtual distributed control system technology and dynamic process modeling theory. Furthermore, we implemented the digital twin system and the prototype of the industrial Turing test in the industrial gas industry. Industrial practice shows that the proposed industrial Turing test provides an approach to test the effectiveness of the process industrial digital twin system.

Constant attempts have been made throughout human history to find solutions to complex issues. These attempts resulted in industrial revolutions and the transition from manual labour to the use of machines and new technologies. The latest advances in Artificial Intelligence (AI) are revolutionary. The use of these smart technologies in mining can lead to increased profitability, enhanced performance, improved safety, and better adherence to environmental regulations. In this paper, the applications of AI and digital twin systems in mining operations are reviewed, covering various components, including mineral exploration, drilling, blasting, loading, hauling, mineral processing, and environmental issues. Critical data inputs for each component are identified, and relevant tools and methods are discussed. These will be used to facilitate the development of digital twin models with the capabilities of learning, simulation, prediction, and optimisation. This study provides valuable insights into fully integrated digital twin mining systems, which will significantly improve mining efficiency and sustainability. Although innovative technologies, such as IoT and other intelligent tools, are increasingly being used in the mining sector, many mining processes still depend on human oversight to deal with challenges such as remote operations, geological variability, high investment costs, and a skills gap. There is, therefore, significant potential to enhance the use of sensors and IoT devices to support data collection for more integrated and powerful digital twin systems, to drive further innovation and operational improvements across the mining value chain.

This paper explores the integration of Artificial Intelligence (AI) and Digital Twin systems in the classroom teaching of engineering courses, with a focus on the Industrial Robot Technology course1. It addresses the challenges of traditional practical teaching methods and proposes a new model that leverages AI and Digital Twin technology to enhance student engagement, improve learning outcomes, and better prepare students for the demands of the industry. The paper discusses the implementation of AI-assisted teaching, the creation of a ubiquitous teaching ecology, and the strengthening of vocational skills through digital twin.

Explainable, interpretable, and trustworthy AI for an intelligent digital twin: A case study on remaining useful life

Rapid diagnosis and therapeutic monitoring of aggressive diseases such as glioblastoma can improve patient survival by providing physicians the time to optimally deliver treatment. This research tested whether metabolic imaging with hyperpolarized MRI could detect changes in tumor progression faster than conventional anatomic MRI in patient-derived glioblastoma murine models. To capture the dynamic nature of cancer metabolism, hyperpolarized MRI, NMR spectroscopy, and immunohistochemistry were performed at several time-points during tumor development, regression, and recurrence. Hyperpolarized MRI detected significant changes of metabolism throughout tumor progression whereas conventional MRI was less sensitive. This was accompanied by aberrations in amino acid and phospholipid lipid metabolism and MCT1 expression. Hyperpolarized MRI can help address clinical challenges such as identifying malignant disease prior to aggressive growth, differentiating pseudoprogression from true progression, and predicting relapse. The individual evolution of these metabolic assays as well as their correlations with one another provides context for further academic research.

Study question Is it safe to use metabolic imaging to measure nicotinamide adenine dinucleotide (NADH) associated auto-fluorescence during embryo development using adapted confocal microscopy? Summary answer Non-invasive metabolic imaging is safe as no differences were observed between controls and illuminated embryos in terms of embryo development, blastocyst formation and implantation potential. What is known already Developing non-invasive methods that are reliable to assess oocyte and embryo quality has been a significant aim for assisted reproductive technologies. Changes in metabolic activity could lead to cell death or abnormal embryo development and low implantation potential. This could potentially be predicted by incorporating non-invasive measurements of metabolism. Metabolic imaging in embryos has been investigated through complex methodologies, however, scientific evidence for its utility during embryo development using simple technology remains unexplored. Measurements of metabolic activity could be a useful tool as the auto-fluorescence of molecules such as NADH is a straightforward representation of mitochondrial function. Study design, size, duration Super-ovulated female mice (n = 30) were subjected to mating with 10 males. In-vivo produced embryos collected at the 2-cell stage were divided in control group (n = 151), sham control group (n = 151) and illuminated group (n = 152). Illuminated embryos were assessed for NADH levels during embryo development every 3 hours using arbitrary units of autofluorescence (AU). Produced blastocysts were assessed for total cell and inner-cell-mass (ICM) number (Oct4 immuno-staining) and implantation potential through outgrowth assays in separate experiments. Participants/materials, setting, methods F1 (CBA/C57Bl6) mouse strain was used. NADH auto-fluorescence levels were measured during embryo development using adapted confocal microcopy (Olympus FV1200). A confocal Z-stacking function was used to record 15 focal planes using a 20x/0.95NA air objective of entire embryos, opening the confocal pinhole system completely. Then, images were collected and analysed using FIJI software (version: 2.0.0-rc-69/1.52n;ImageJ). Blastocyst cell number, formation rates and outgrowth rates for 4 days post blastocyst formation were compared between study groups. Main results and the role of chance Embryo culture experiments showed no significant differences in blastocyst formation rates between study groups (Control: 71.7%; Sham: 64.9%; Illuminated 71.7%; p > 0.05). Similarly, the total number of cells (Control: 82.9±5.6; Sham: 76.5±3.3; Illuminated: 77.1±4.2; ± Standard error of mean [SEM]) and ICM cells (Control: 10.8±1.3; Sham: 9.4±0.7; Illuminated: 11.9±0.8; ± SEM) did not differ between groups (p > 0.05). Outgrowth assays presented similar outgrowth areas during day5 to day8 post-blastocyst development between study groups (p > 0.05). Illuminated embryos presented significantly different NADH activity levels during embryo development, particularly between the 2-cell stage (987.1±36.2AU), morulae stage (1226±31.5AU) and blastocyst stage (649±42.9AU; ± SEM; p < 0.05). Embryos that did not reach the blastocyst stage presented a significantly different NADH activity profile during embryo development compared to those that did(p < 0.05). Additionally, abnormal embryos also presented significantly decreased NADH activity levels at the 2-cell stage (Normal: 987.1±36.2; abnormal: 726.9±121.7AU; p < 0.05) to the morulae stage (Normal: 1226±31.5; Abnormal:893.3±189AU; p < 0.05). Our study indicates that measuring NADH activity levels during early embryo development present no negative effects in embryo developmental rates, blastocyst formation and implantation potential. Thus, non-invasive measurements of NADH could be applied to determine embryo metabolic activity during embryo development using simple technology and imaging techniques. Limitations, reasons for caution The study was conducted using a mouse model focused in early embryo development and implantation potential. Thus, studies on live birth are required to fully assess safety to further validate potential wider applications. Validation in ageing models is also required to assess potential applications for embryo selection. Wider implications of the findings Non-invasive measurements of metabolic activity could be applied to determine embryo metabolic activity using simple and safe technology. Further applications could link the use of simple non-invasive metabolic imaging with the latest time-lapse technology and artificial intelligence applications. Trial registration number N/A

The concept of a digital twin (DT) has gained significant attention in academia and industry because of its perceived potential to address critical global challenges, such as climate change, healthcare, and economic crises. Originally introduced in manufacturing, many attempts have been made to present proper definitions of this concept. Unfortunately, there remains a great deal of confusion surrounding the underlying concept, with many scientists still uncertain about the distinction between a simulation, a mathematical model and a DT. The aim of this paper is to propose a formal definition of a digital twin. To achieve this goal, we utilize a data science framework that facilitates a functional representation of a DT and other components that can be combined together to form a larger entity we refer to as a digital twin system (DTS). In our framework, a DT is an open dynamical system with an updating mechanism, also referred to as complex adaptive system (CAS). Its primary function is to generate data via simulations, ideally, indistinguishable from its physical counterpart. On the other hand, a DTS provides techniques for analyzing data and decision-making based on the generated data. Interestingly, we find that a DTS shares similarities to the principles of general systems theory. This multi-faceted view of a DTS explains its versatility in adapting to a wide range of problems in various application domains such as engineering, manufacturing, urban planning, and personalized medicine.

Chapter 6 - Digital twins and artificial intelligence: transforming industrial operations

This paper provides a comprehensive overview of the evolving intersection between Space Medicine and Artificial Intelligence (AI), tracing its journey from nascent conceptualizations to its current state and projecting future trends. For the purposes of this overview, only specific currently available AI methodologies will be used. These include Machine Learning, Deep Learning, Convolution Neural Networks, Recurrent Neural Networks, and Natural Language Processing. Initially, this exploration delves into the historical context, examining how early space missions recognized the need for medical monitoring and support, and the rudimentary role early forms of AI played in these stages. The paper then transitions to the present, highlighting current advancements where AI has become integral in diagnosing and managing health issues in space, optimizing life support systems, and enhancing astronauts’ physical and psychological well-being. Significant focus is placed on current AI-driven technologies, such as predictive algorithms for health risks, robotic surgical tools, and AI-assisted mental health support. Looking ahead, the paper explores potential future developments, envisioning a scenario where AI not only augments space medicine but becomes a critical component in long-duration interplanetary missions. This includes AI’s role in autonomous medical systems, personalized medicine, and in addressing the unique challenges of deep space travel. The paper concludes with a discussion on the ethical, logistical, and technical challenges that lie ahead, emphasizing the need for robust, ethically guided AI frameworks to ensure the safety and health of astronauts as humanity ventures further into the cosmos.

_ The rapid development of oil and gas intelligent operations depends on artificial intelligence (AI), automation, and data analytics to achieve optimal conditions in oil and gas operations. Digital-twin technology uses virtual copies of physical assets to perform complex analyses that improve performance while actively identifying mechanical failures before they materialize. Revolutionary drone and robotic technologies transform field operations by enabling autonomous systems to perform critical inspections in hazardous environments. These solutions eliminate hazardous conditions for workers, develop comprehensive data acquisition systems, and achieve cost-effective operation. The autonomous systems use advanced sensors and AI-powered image recognition to spot tiny faults in infrastructure and track environmental conditions while delivering critical operational data. Cloud computing, alongside industrial Internet of Things (IIoT) platforms, provides exceptional capabilities for data consolidation. The connected ecosystems enable businesses to distribute operational data between upstream, midstream, and downstream sectors, thus eliminating functional divisions and enabling data-driven group decisions. The drive for sustainability enables the technological development of AI and advanced analytics systems that strengthen carbon-capture approaches and reduce energy usage and supervisory methane releases. Modern systems are used more frequently to match operational developments and environmental targets. Cybersecurity is becoming a strategic imperative. The rapid digital transformation of operations leads to advancements in blockchain and encryption that create protected, transparent supply-chain data management. Innovative solutions develop from technology suppliers working with software companies to establish new partnerships with traditional oil and gas businesses. Businesses that work together to create integrated technological systems that use machine learning, predictive analytics, and autonomous systems establish new operational standards. The energy industry stands on the verge of a technical breakthrough enabled by the advancement of AI and analytics that will create new efficiencies and safety standards for intelligent energy management in the future. Summarized Papers in This May 2025 Issue SPE 221158 - Digital Transformation Leads to Smart Production Surveillance in Amazon Brownfield by Roberto Fuenmayor, Hugo Quevedo, and Christian Bonilla, SLB, et al. IPTC 23455 - Immersive Collaboration Platform Accelerates Upstream Growth by Badr Al-Harbi, Muhammad Al-Readean, and Amell Al-Ghamdi, Saudi Aramco, et al. OTC 35351 - Digital-Twin System Provides Model-Based Operational Support by Chinenye E. Ogugbue, Zachary M. Greenawalt, and Yevgeniy Kondratenko, BP, et al. Recommended Additional Reading SPE 220932 - Success Cases and Lessons Learned After 20 Years of Oilfield Digitalization Efforts by L. Saputelli, Frontender, et al. SPE 222876 - An Integrated Solution for Operational Planning in an Intelligent Underground Gas Storage System by H. Jiang, Chongquing XiangGuoSi Underground Gas Storage, et al. SPE 223450 - Agile Delivery of Business Through Integrated Planning, Application of Minimum Functional Objective, and Digitization by K. Ussenbayeva, Tengizchevroil, et al.

Precisely measuring tumor-associated alterations in metabolism clinically will enable the efficient assessment of therapeutic responses. Advances in imaging technologies can exploit the differences in cancer-associated cell metabolism as compared to normal tissue metabolism, linking changes in target metabolism to therapeutic efficacy. Metabolic imaging by Positron Emission Tomography (PET) employing 2-fluoro-deoxy-glucose ([18F]FDG) has been used as a routine diagnostic tool in the clinic. Recently developed hyperpolarized Magnetic Resonance (HP-MR), which radically increases the sensitivity of conventional MRI, has created a renewed interest in functional and metabolic imaging. The successful translation of this technique to the clinic was achieved recently with measurements of 13C-pyruvate metabolism. Here, we review the potential clinical roles for metabolic imaging with hyperpolarized MRI as applied in assessing therapeutic intervention in different cancer systems.