Digital Twins for Multiple Sclerosis

Scoping review of clinical decision support systems for multiple sclerosis management: Leveraging information technology and massive health data

BACKGROUND AND PURPOSE

Multiple sclerosis (MS) is a complex autoimmune disease of the central nervous system, with numerous therapeutic options, but a lack of biomarkers to support a mechanistic approach to precision medicine. A computational approach to precision medicine could proceed from clinical decision support systems (CDSSs). They are digital tools aiming to empower physicians through the clinical applications of information technology and massive data. However, the process of their clinical development is still maturing; we aimed to review it in the field of MS.

METHODS

For this scoping review, we screened systematically the PubMed database. We identified 24 articles reporting 14 CDSS projects and compared their technical and software development aspects.

RESULTS

The projects position themselves in various contexts of usage with various algorithmic approaches: expert systems, CDSSs based on similar patients' data visualization, and model-based CDSSs implementing mathematical predictive models. So far, no project has completed its clinical development up to certification for clinical use with global release. Some CDSSs have been replaced at subsequent project iterations. The most advanced projects did not necessarily report every step of clinical development in a dedicated article (proof of concept, offline validation, refined prototype, live clinical evaluation, comparative prospective evaluation). They seek different software distribution options to integrate into health care: internal usage, "peer-to-peer," and marketing distribution.

CONCLUSIONS

This review illustrates the potential of clinical applications of information technology and massive data to support MS management and helps clarify the roadmap for future projects as a multidisciplinary and multistep process.

Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review

Pain is a significant health issue, and pain assessment is essential for proper diagnosis, follow-up, and effective management of pain. The conventional methods of pain assessment often suffer from subjectivity and variability. The main issue is to understand better how people experience pain. In recent years, artificial intelligence (AI) has been playing a growing role in improving clinical diagnosis and decision-making. The application of AI offers promising opportunities to improve the accuracy and efficiency of pain assessment. This review article provides an overview of the current state of AI in pain assessment and explores its potential for improving accuracy, efficiency, and personalized care. By examining the existing literature, research gaps, and future directions, this article aims to guide further advancements in the field of pain management. An online database search was conducted via multiple websites to identify the relevant articles. The inclusion criteria were English articles published between January 2014 and January 2024). Articles that were available as full text clinical trials, observational studies, review articles, systemic reviews, and meta-analyses were included in this review. The exclusion criteria were articles that were not in the English language, not available as free full text, those involving pediatric patients, case reports, and editorials. A total of (47) articles were included in this review. In conclusion, the application of AI in pain management could present promising solutions for pain assessment. AI can potentially increase the accuracy, precision, and efficiency of objective pain assessment.

Sufficiency for PSS tracking gait disorders in multiple sclerosis: A managerial perspective

This study primarily aimed to explore the capabilities of digitalisation in the healthcare context, focusing on a specific disease. In this case, the study examined the potential of remote monitoring of gait to address the sensitivity of multiple sclerosis progression to gait characteristics by adopting a non-invasive approach to remotely quantify gait disturbances in a patient's daily life. To better understand the managerial aspects associated with this approach, the researchers conducted a literature review along with a set of semi-structured interviews. The target population included MS patients as well as the key agents involved in their care: patients' family members, neurologists, MS nurses, physiotherapists, medical directors, and pharmacist. The study identifies the perceived barriers and drivers that could contribute to the successful deployment of PSS remote gait monitoring as a healthcare service: i) At mega-level governance. Implications on privacy and security data are notable barriers missing on the speech. ii) At macro level, funding is highlighted as main barrier. The cost and lack of health system subsidies may render initiatives unsustainable, as emphasised by the interviewees. iii) At meso level, useable data is recognised as a driver. The data collection process can align with diverse interests to create value and business opportunities for the ecosystem actors, enhance care, attract stakeholders, such as insurers and pharma, and form partnerships. iv) At micro-level processes, we find two potential barriers: wearable device and app usability (comfort, navigation, efficiency) and organisational/behavioural aspects (training, digital affinity, skills), which are crucial for value creation in innovation ecosystems among patients and healthcare professionals. Finally, we find an interesting gap in the literature and interviews. Stakeholders' limited awareness of technological demands, especially from information technologies, for a successful long-term service, can be consider two key barriers for PSS.

Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins

Digital twins have emerged as a groundbreaking concept in personalized medicine, offering immense potential to transform health care delivery and improve patient outcomes. It is important to highlight the impact of digital twins on personalized medicine across the understanding of patient health, risk assessment, clinical trials and drug development, and patient monitoring. By mirroring individual health profiles, digital twins offer unparalleled insights into patient-specific conditions, enabling more accurate risk assessments and tailored interventions. However, their application extends beyond clinical benefits, prompting significant ethical debates over data privacy, consent, and potential biases in health care. The rapid evolution of this technology necessitates a careful balancing act between innovation and ethical responsibility. As the field of personalized medicine continues to evolve, digital twins hold tremendous promise in transforming health care delivery and revolutionizing patient care. While challenges exist, the continued development and integration of digital twins hold the potential to revolutionize personalized medicine, ushering in an era of tailored treatments and improved patient well-being. Digital twins can assist in recognizing trends and indicators that might signal the presence of diseases or forecast the likelihood of developing specific medical conditions, along with the progression of such diseases. Nevertheless, the use of human digital twins gives rise to ethical dilemmas related to informed consent, data ownership, and the potential for discrimination based on health profiles. There is a critical need for robust guidelines and regulations to navigate these challenges, ensuring that the pursuit of advanced health care solutions does not compromise patient rights and well-being. This viewpoint aims to ignite a comprehensive dialogue on the responsible integration of digital twins in medicine, advocating for a future where technology serves as a cornerstone for personalized, ethical, and effective patient care.

From progression to progress: The future of multiple sclerosis

Significant advances have been made in the diagnosis and treatment of multiple sclerosis in recent years yet challenges remain. The current classification of MS phenotypes according to disease activity and progression, for example, does not adequately reflect the underlying pathophysiological mechanisms that may be acting in an individual with MS at different time points. Thus, there is a need for clinicians to transition to a management approach based on the underlying pathophysiological mechanisms that drive disability in MS. A Canadian expert panel convened in January 2023 to discuss priorities for clinical discovery and scientific exploration that would help advance the field. Five key areas of focus included: identifying a mechanism-based disease classification system; developing biomarkers (imaging, fluid, digital) to identify pathologic processes; implementing a data-driven approach to integrate genetic/environmental risk factors, clinical findings, imaging and biomarker data, and patient-reported outcomes to better characterize the many factors associated with disability progression; utilizing precision-based treatment strategies to target different disease processes; and potentially preventing disease through Epstein-Barr virus (EBV) vaccination, counselling about environmental risk factors (e.g. obesity, exercise, vitamin D/sun exposure, smoking) and other measures. Many of the tools needed to meet these needs are currently available. Further work is required to validate emerging biomarkers and tailor treatment strategies to the needs of individual patients. The hope is that a more complete view of the individual's pathobiology will enable clinicians to usher in an era of truly personalized medicine, in which more informed treatment decisions throughout the disease course achieve better long-term outcomes.

Virtual brain twins: from basic neuroscience to clinical use

Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions.

Consensus quality indicators for monitoring multiple sclerosis

Multiple sclerosis (MS) as a chronic, degenerative autoimmune disease of the central nervous system has a longitudinal and heterogeneous course with increasing treatment options and risk profiles requiring constant monitoring of a growing number of parameters. Despite treatment guidelines, there is a lack of strategic and individualised monitoring pathways, including respective quality indicators (QIs). To address this, we systematically developed transparent, traceable, and measurable QIs for MS monitoring. Through literature review, expert discussions, and consensus-building, existing QIs were identified and refined. In a two-stage online Delphi process involving MS specialists (on average 53 years old and with 25 years of professional experience), the QIs were evaluated for content, clarity, and intelligibility, resulting in a set of 24 QIs and checklists to assess the quality of care. The final QIs provide a structured approach to document, monitor, and enhance the quality of care for people with MS across their treatment journey.

The Association of Age, Sex, and BMI on Lower Limb Neuromuscular and Muscle Mechanical Function in People with Multiple Sclerosis

(1) Background: The countermovement jump (CMJ) on a force plate could be a sensitive assessment for detecting early lower-limb muscle mechanical deficits in the early stages of multiple sclerosis (MS). CMJ performance is known to be influenced by various anthropometric, physiological, and biomechanical factors, mostly investigated in children and adult athletes. Our aim was to investigate the association of age, sex, and BMI with muscle mechanical function using CMJ to provide a comprehensive overview of lower-limb motor function in people with multiple sclerosis (pwMS). (2) Methods: A cross-sectional study was conducted with pwMS (N = 164) and healthy controls (N = 98). All participants performed three maximal CMJs on a force plate. Age, sex, and BMI were collected from all participants. (3) Results: Significant age, sex, and BMI effects were found for all performance parameters, flight time, and negative and positive power for pwMS and HC, but no significant interaction effects with the group (pwMS, HC) were detected. The highest significant effects were found for sex on flight time (η2 = 0.23), jump height (η2 = 0.23), and positive power (η2 = 0.13). PwMS showed significantly lower CMJ performance compared to HC in middle-aged (31-49 years), with normal weight to overweight and in both women and men. (4) Conclusions: This study showed that age, sex, and BMI are associated with muscle mechanical function in pwMS and HC. These results may be useful in developing reference values for CMJ. This is a crucial step in integrating CMJ into the diagnostic assessment of people with early MS and developing individualized and effective neurorehabilitative therapy.

Sensitive Identification of Asymmetries and Neuromuscular Deficits in Lower Limb Function in Early Multiple Sclerosis

BACKGROUND

In the early stages of multiple sclerosis (MS), there are no objective sensitive functional assessments to identify and quantify early subclinical neuromuscular deficits and lower limb strength asymmetries during complex movements. Single-countermovement jumps (SLCMJ), a maximum single leg vertical jump, on a force plate allow functional evaluation of unilateral lower limb performance in performance diagnostics and could therefore provide early results on asymmetries in MS.

OBJECTIVE

Objective evaluation of early lower limb neuromuscular deficits and asymmetries in people with multiple sclerosis (pwMS) using SLCMJ on a force plate.

METHODS

A study was conducted with pwMS (N = 126) and healthy controls (N = 97). All participants performed 3 maximal SLCMJs on a force plate. Temporal, kinetic, and power jump parameters were collected. The Expanded Disability Status Scale (EDSS) was performed on all participants. A repeated measures analysis of covariance (ANCOVA) with age, Body-Mass-Index, and gender as covariates was used.

RESULTS

PwMS with normal muscle strength according to the manual muscle tests showed significantly reduced SLCMJ performance compared to HC. In both groups, jumping performance differed significantly between the dominant and non-dominant leg, with higher effect size for pwMS. A significant interaction effect between leg dominance and group was found for propulsive time, where the pwMS showed an even higher difference between the dominant and non-dominant leg compared to HC. Furthermore, there was a significant small correlation between leg asymmetries and EDSS in pwMS.

CONCLUSION

The study shows that the SLCMJ on a force plate is suitable for the early detection of subclinical lower limb neuromuscular deficits and strength asymmetries in MS.

Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software

PURPOSE

The assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three centers study, for the assessment of new, slowly expanding, and contrast-enhancing MS lesions.

METHODS

In three separate centers, 117 MS follow-up MRIs were blindly analyzed on fluid attenuated inversion recovery (FLAIR), pre- and post-gadolinium T1-weighted images using Jazz by 2 neuroradiologists in each center. The reading time was recorded. The ground truth was defined in a second reading by side-by-side comparison of both reports from Jazz and the standard clinical report. The number of described new, slowly expanding, and contrast-enhancing lesions described with Jazz was compared to the lesions described in the standard clinical report.

RESULTS

A total of 96 new lesions from 41 patients and 162 slowly expanding lesions (SELs) from 61 patients were described in the ground truth reading. A significantly larger number of new lesions were described using Jazz compared to the standard clinical report (63 versus 24). No SELs were reported in the standard clinical report, while 95 SELs were reported on average using Jazz. A total of 4 new contrast-enhancing lesions were found in all reports. The reading with Jazz was very time efficient, taking on average 2min33s ± 1min0s per case. Overall inter-reader agreement for new lesions between the readers using Jazz was moderate for new lesions (Cohen kappa = 0.5) and slight for SELs (0.08).

CONCLUSION

The quality and the productivity of neuroradiological reading of MS follow-up MRI scans can be significantly improved using the dedicated software Jazz.

Building digital patient pathways for the management and treatment of multiple sclerosis

Recent advances in the field of artificial intelligence (AI) could yield new insights into the potential causes of multiple sclerosis (MS) and factors influencing its course as the use of AI opens new possibilities regarding the interpretation and use of big data from not only a cross-sectional, but also a longitudinal perspective. For each patient with MS, there is a vast amount of multimodal data being accumulated over time. But for the application of AI and related technologies, these data need to be available in a machine-readable format and need to be collected in a standardized and structured manner. Through the use of mobile electronic devices and the internet it has also become possible to provide healthcare services from remote and collect information on a patient's state of health outside of regular check-ups on site. Against this background, we argue that the concept of pathways in healthcare now could be applied to structure the collection of information across multiple devices and stakeholders in the virtual sphere, enabling us to exploit the full potential of AI technology by e.g., building digital twins. By going digital and using pathways, we can virtually link patients and their caregivers. Stakeholders then could rely on digital pathways for evidence-based guidance in the sequence of procedures and selection of therapy options based on advanced analytics supported by AI as well as for communication and education purposes. As far as we aware of, however, pathway modelling with respect to MS management and treatment has not been thoroughly investigated yet and still needs to be discussed. In this paper, we thus present our ideas for a modular-integrative framework for the development of digital patient pathways for MS treatment.

Health Digital Twins in Life Science and Health Care Innovation

Health digital twins (HDTs) are virtual representations of real individuals that can be used to simulate human physiology, disease, and drug effects. HDTs can be used to improve drug discovery and development by providing a data-driven approach to inform target selection, drug delivery, and design of clinical trials. HDTs also offer new applications into precision therapies and clinical decision making. The deployment of HDTs at scale could bring a precision approach to public health monitoring and intervention. Next steps include challenges such as addressing socioeconomic barriers and ensuring the representativeness of the technology based on the training and validation data sets. Governance and regulation of HDT technology are still in the early stages.

Assessing the benefits of digital twins in neurosurgery: a systematic review

Digital twins are virtual replicas of their physical counterparts, and can assist in delivering personalized surgical care. This PRISMA guideline-based systematic review evaluates current literature addressing the effectiveness and role of digital twins in many stages of neurosurgical management. The aim of this review is to provide a high-quality analysis of relevant, randomized controlled trials and observational studies addressing the neurosurgical applicability of a variety of digital twin technologies. Using pre-specified criteria, we evaluated 25 randomized controlled trials and observational studies on the applications of digital twins, including navigation, robotics, and image-guided neurosurgeries. All 25 studies compared these technologies against usual surgical approaches. Risk of bias analyses using the Cochrane risk of bias tool for randomized trials (Rob 2) found "low" risk of bias in the majority of studies (23/25). Overall, this systematic review shows that digital twin applications have the potential to be more effective than conventional neurosurgical approaches when applied to brain and spinal surgery. Moreover, the application of these novel technologies may also lead to fewer post-operative complications.

Digital patient twins for personalized therapeutics and pharmaceutical manufacturing

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.

On the role of benchmarking data sets and simulations in method comparison studies

Method comparisons are essential to provide recommendations and guidance for applied researchers, who often have to choose from a plethora of available approaches. While many comparisons exist in the literature, these are often not neutral but favor a novel method. Apart from the choice of design and a proper reporting of the findings, there are different approaches concerning the underlying data for such method comparison studies. Most manuscripts on statistical methodology rely on simulation studies and provide a single real-world data set as an example to motivate and illustrate the methodology investigated. In the context of supervised learning, in contrast, methods are often evaluated using so-called benchmarking data sets, that is, real-world data that serve as gold standard in the community. Simulation studies, on the other hand, are much less common in this context. The aim of this paper is to investigate differences and similarities between these approaches, to discuss their advantages and disadvantages, and ultimately to develop new approaches to the evaluation of methods picking the best of both worlds. To this aim, we borrow ideas from different contexts such as mixed methods research and Clinical Scenario Evaluation.

A digital twin model for evidence-based clinical decision support in multiple myeloma treatment

The treatment landscape for multiple myeloma (MM) has experienced substantial progress over the last decade. Despite the efficacy of new substances, patient responses tend to still be highly unpredictable. With increasing cognitive burden that is introduced through a complex and evolving treatment landscape, data-driven assistance tools are becoming more and more popular. Model-based approaches, such as digital twins (DT), enable simulation of probable responses to a set of input parameters based on retrospective observations. In the context of treatment decision-support, those mechanisms serve the goal to predict therapeutic outcomes to distinguish a favorable option from a potential failure. In the present work, we propose a similarity-based multiple myeloma digital twin (MMDT) that emphasizes explainability and interpretability in treatment outcome evaluation. We've conducted a requirement specification process using scientific literature from the medical and methodological domains to derive an architectural blueprint for the design and implementation of the MMDT. In a subsequent stage, we've implemented a four-layer concept where for each layer, we describe the utilized implementation procedure and interfaces to the surrounding DT environment. We further specify our solutions regarding the adoption of multi-line treatment strategies, the integration of external evidence and knowledge, as well as mechanisms to enable transparency in the data processing logic. Furthermore, we define an initial evaluation scenario in the context of patient characterization and treatment outcome simulation as an exemplary use case for our MMDT. Our derived MMDT instance is defined by 475 unique entities connected through 438 edges to form a MM knowledge graph. Using the MMRF CoMMpass real-world evidence database and a sample MM case, we processed a complete outcome assessment. The output shows a valid selection of potential treatment strategies for the integrated medical case and highlights the potential of the MMDT to be used for such applications. DT models face significant challenges in development, including availability of clinical data to algorithmically derive clinical decision support, as well as trustworthiness of the evaluated treatment options. We propose a collaborative approach that mitigates the regulatory and ethical concerns that are broadly discussed when automated decision-making tools are to be included into clinical routine.

CONNECTED: leveraging digital twins and personal knowledge graphs in healthcare digitalization

Healthcare has always been a strategic domain in which innovative technologies can be applied to increase the effectiveness of services and patient care quality. Recent advancements have been made in the adoption of Digital Twins (DTs) and Personal Knowledge Graphs (PKGs) in this field. Despite this, their introduction has been hindered by the complex nature of the context itself which leads to many challenges both technical and organizational. In this article, we reviewed the literature about these technologies and their integrations, identifying the most critical requirements for clinical platforms. These latter have been used to design CONNECTED (COmpreheNsive and staNdardized hEalth-Care plaTforms to collEct and harmonize clinical Data), a conceptual framework aimed at defining guidelines to overcome the crucial issues related to the development of healthcare applications. It is structured in a multi-layer shape, in which heterogeneous data sources are first integrated, then standardized, and finally used to realize general-purpose DTs of patients backed by PKGs and accessible through dedicated APIs. These DTs will be the foundation on which smart applications can be built.

[Digital twins in healthcare: State of the art and potential use cases in a hospital setting]

The field of modeling and simulation including digital twins (DT) is getting a lot of traction in the health sector, with various benefits being outlined. Indeed, DT are designed to support the transformation of healthcare, ranging from the medical act itself to the organization of healthcare systems and hospitals, also including medical research. However, the technology is still in its infancy and needs to be evaluated according to medical standards. First and foremost, their clinical value remains to be assessed, taking into account the limits associated with AI tools. The aim of this review is to provide an overview of the use cases being tested in hospital settings. We go back to the definition of the DT concept and provide a summary of current studies. Finally, we outline the challenges to be taken into account in order to provide a framework for the deployment of DT tools, guaranteeing the trust and safety of those involved, especially of patients.

Management of multiple sclerosis fatigue in the digital age: from assessment to treatment

Fatigue is one of the most disabling symptoms of Multiple Sclerosis (MS), affecting more than 80% of patients over the disease course. Nevertheless, it has a multi-faceted and complex nature, making its diagnosis, evaluation, and treatment extremely challenging in clinical practice. In the last years, digital supporting tools have emerged to support the care of people with MS. These include not only smartphone or table-based apps, but also wearable devices or novel techniques such as virtual reality. Furthermore, an additional effective and cost-efficient tool for the therapeutic management of people with fatigue is becoming increasingly available. Virtual reality and e-Health are viable and modern tools to both assess and treat fatigue, with a variety of applications and adaptability to patient needs and disability levels. Most importantly, they can be employed in the patient's home setting and can not only bridge clinic visits but also be complementary to the monitoring and treatment means for those MS patients who live far away from healthcare structures. In this narrative review, we discuss the current knowledge and future perspectives in the digital management of fatigue in MS. These may also serve as sources for research of novel digital biomarkers in the identification of disease activity and progression.

Brave New Healthcare: A Narrative Review of Digital Healthcare in American Medicine

The digital revolution has had a profound effect on American and global healthcare, which was accelerated by the pandemic and telehealth applications. Digital health also includes popular and more esoteric forms of wearable monitoring systems and interscatter and other wireless technologies that facilitate their telemetry. The rise in artificial intelligence (AI) and machine learning (ML) may serve to improve interpretation from imaging technologies to electrocardiography or electroencephalographic tracings, and new ML techniques may allow these systems to scan data to discern and contextualize patterns that may have evaded human physicians. The necessity of virtual care during the pandemic has morphed into new treatment paradigms, which have gained patient acceptance but still raise issues with respect to privacy laws and credentialing. Augmented and virtual reality tools can facilitate surgical planning and "hands-on" clinical training activities. Patients are working with new frontiers in digital health in the form of "Dr. Google" and patient support websites to learn or share medical information. Patient-facing digital health information is both a blessing and curse, in that it can be a boon to health-literate patients who seek to be more active in their own care. On the other hand, digital health information can lead to false conclusions, catastrophizing, misunderstandings, and "cyberchondria." The role of blockchain, familiar from cryptocurrency, may play a role in future healthcare information and would serve as a disruptive, decentralizing, and potentially beneficial change. These important changes are both exciting and perplexing as clinicians and their patients learn to navigate this new system and how we address the questions it raises, such as medical privacy in a digital age. The goal of this review is to explore the vast range of digital health and how it may impact the healthcare system.

Toward precision medicine using a "digital twin" approach: modeling the onset of disease-specific brain atrophy in individuals with multiple sclerosis

Digital Twin (DT) is a novel concept that may bring a paradigm shift for precision medicine. In this study we demonstrate a DT application for estimating the age of onset of disease-specific brain atrophy in individuals with multiple sclerosis (MS) using brain MRI. We first augmented longitudinal data from a well-fitted spline model derived from a large cross-sectional normal aging data. Then we compared different mixed spline models through both simulated and real-life data and identified the mixed spline model with the best fit. Using the appropriate covariate structure selected from 52 different candidate structures, we augmented the thalamic atrophy trajectory over the lifespan for each individual MS patient and a corresponding hypothetical twin with normal aging. Theoretically, the age at which the brain atrophy trajectory of an MS patient deviates from the trajectory of their hypothetical healthy twin can be considered as the onset of progressive brain tissue loss. With a tenfold cross validation procedure through 1000 bootstrapping samples, we found the onset age of progressive brain tissue loss was, on average, 5-6 years prior to clinical symptom onset. Our novel approach also discovered two clear patterns of patient clusters: earlier onset versus simultaneous onset of brain atrophy.

Implications of immersive technologies in healthcare sector and its built environment

Objectives

This research focuses on how built environment experts can contribute to the MXR-enabled digital innovation as part of the multidisciplinary team effort to ensure post-pandemic resilience in healthcare built environment. The goal of this research is to help healthcare providers, built environment experts, and policy makers respectively: (1) Advocate the benefits of MXR for innovating health and social care; (2) Spark debate across networks of expertise to create health-promoting environment; and (3) Understand the overriding priorities in making effective pathways to the implementation of MXR.

Methods

To highlight the novelty of this research, the study relies on two qualitative methodologies: exploratory literature review and semi-structured interviews. Based on the evaluation of prior works and cross-national case studies, hypotheses are formulated from three arenas: (1) Cross-sectional Initiatives for Post-pandemic Resilience; (2) Interoperability and Usability of Next-gen Medicines; and (3) Metaverse and New Forms of Value in Future Healthcare Ecosystems. To verify those hypotheses, empirical findings are derived from in-depth interviews with nine key informants.

Results

The main findings are summarized under the following three themes: (1) Synergism between Architecture and Technology; (2) Patient Empowerment and Staff Support; and (3) Scalable Health and Wellbeing in Non-hospital and Therapeutic Settings. Firstly, both built environment and healthcare sectors can benefit from the various capabilities of MXR through cross-sectional initiatives, evidence-based practices, and participatory approaches. Secondly, a confluence of knowledge and methods of HCI and HBI can increase the interoperability and usability of MXR for the patient-centered and value-based healthcare models. Thirdly, the MXR-enabled technological regime will largely affect the new forms of value in healthcare premises by fostering more decentralized, preventive, and therapeutic characteristics in the future healthcare ecosystems.

Conclusion

Whether it's virtual or physical, our healthcare systems have placed great emphasis on the rigor of evidence-based approach linking health outcome to a clinical environment. Henceforth, built environment experts should seek closer ties with the MXR ecosystems for the co-production of scalable health and wellbeing in non-hospital and therapeutic settings. Ultimately, this is to improve resource efficiency in the healthcare sector while considering the transition of health resources towards in silico status by increasing the implementation of MXR.

Digital twin for healthcare systems

Digital twin technology is revolutionizing healthcare systems by leveraging real-time data integration, advanced analytics, and virtual simulations to enhance patient care, enable predictive analytics, optimize clinical operations, and facilitate training and simulation. With the ability to gather and analyze a wealth of patient data from various sources, digital twins can offer personalized treatment plans based on individual characteristics, medical history, and real-time physiological data. Predictive analytics and preventive interventions are made possible by machine learning algorithms, allowing for early detection of health risks and proactive interventions. Digital twins can optimize clinical operations by analyzing workflows and resource allocation, leading to streamlined processes and improved patient care. Moreover, digital twins can provide a safe and realistic environment for healthcare professionals to enhance their skills and practice complex procedures. The implementation of digital twin technology in healthcare has the potential to significantly improve patient outcomes, enhance patient safety, and drive innovation in the healthcare industry.

Artificial Intelligence and Multiple Sclerosis: Up-to-Date Review

Multiple sclerosis (MS) remains a challenging neurological disorder for the clinician in terms of diagnosis and management. The growing integration of AI-based algorithms in healthcare offers a golden opportunity for clinicians and patients with MS. AI models are based on statistical analyses of large quantities of data from patients including "demographics, genetics, clinical and radiological presentation." These approaches are promising in the quest for greater diagnostic accuracy, tailored management plans, and better prognostication of disease. The use of AI in multiple sclerosis represents a paradigm shift in disease management. With ongoing advancements in AI technologies and the increasing availability of large-scale datasets, the potential for further innovation is immense. As AI continues to evolve, its integration into clinical practice will play a vital role in improving diagnostics, optimizing treatment strategies, and enhancing patient outcomes for MS. This review is about conducting a literature review to identify relevant studies on AI applications in MS. Only peer-reviewed studies published in the last four years have been selected. Data related to AI techniques, advancements, and implications are extracted. Through data analysis, key themes and tendencies are identified. The review presents a cohesive synthesis of the current state of AI and MS, highlighting potential implications and new advancements.

Literature review of digital twin in healthcare

This article aims to make a bibliometric literature review using systematic scientific mapping and content analysis of digital twins in healthcare to know the evolution, domain, keywords, content type, and kind and purpose of digital twin's implementation in healthcare, so a consolidation and future improvement of existing knowledge can be made and gaps for new studies can be identified. The increase in publications of digital twins in healthcare is quite recent and it is still concentrated in the domain of technology sources. The subject is majorly concentrated in patient's digital twin group and in precision medicine and aspects, issues and/or policies subgroups, although the publications keywords mirror it only at the group side. Digital twins in healthcare are probably stepping out of the infancy phase. On the other hand, digital twins in hospital group and the device and facilities management subgroups are more mature with all knowledge gathered from the manufacturing sector. There is an absence of some publication's types in general, device and care subgroup and no whole body or hospital digital twin was reported. Based on the presented arguments, guidelines for future research were presented: advance in the creation of general frameworks, in subgroups not as much explored, and in groups and subgroups already explored, but that need more advancement to achieve the main goals of a whole human or hospital digital twin with the main issues resolved.

Influence of Pre-Analytic Conditions on Quantity of Lymphocytes

Lymphocytes are key players in the pathogenesis of multiple sclerosis and a distinct target of several immunomodulatory treatment strategies. In this study, we aim to evaluate the effect of various pre-analytic conditions on immune cell counts to conclude the relevance for clinical implications. Twenty healthy donors were assessed for the effects of distinct storage temperatures and times after blood draws, different durations of tourniquet application, body positions and varying aspiration forces during blood draws. Immune cell frequencies were analyzed using multicolor flowcytometry. While storage for 24 h at 37 °C after blood draws was associated with significantly lower cell counts, different durations of tourniquet application, body positions and varying aspirations speeds did not have significant impacts on the immune cell counts. Our data suggest that immune cell counts are differently affected by pre-analytic conditions being more sensitive to storage temperature. Pre-analytic conditions should be carefully considered when interpreting the laboratory values of immune cell subpopulations.

Intelligent Digital Twins for Personalized Migraine Care

Intelligent digital twins closely resemble their real-life counterparts. In health and medical care, they enable the real-time monitoring of patients, whereby large amounts of data can be collected to produce actionable information. These powerful tools are constructed with the aid of artificial intelligence, machine learning, and deep learning; the Internet of Things; and cloud computing to collect a diverse range of digital data (e.g., from digital patient journals, wearable sensors, and digitized monitoring equipment or processes), which can provide information on the health conditions and therapeutic responses of their physical twins. Intelligent digital twins can enable data-driven clinical decision making and advance the realization of personalized care. Migraines are a highly prevalent and complex neurological disorder affecting people of all ages, genders, and geographical locations. It is ranked among the top disabling diseases, with substantial negative personal and societal impacts, but the current treatment strategies are suboptimal. Personalized care for migraines has been suggested to optimize their treatment. The implementation of intelligent digital twins for migraine care can theoretically be beneficial in supporting patient-centric care management. It is also expected that the implementation of intelligent digital twins will reduce costs in the long run and enhance treatment effectiveness. This study briefly reviews the concept of digital twins and the available literature on digital twins for health disorders such as neurological diseases. Based on these, the potential construction and utility of digital twins for migraines will then be presented. The potential and challenges when implementing intelligent digital twins for the future management of migraines are also discussed.

Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems

Digital twins are computer programs that use real-world data to create simulations that predict the performance of processes, products, and systems. Digital twins may integrate artificial intelligence to improve their outputs. Models for dealing with uncertainties and noise are used to improve the accuracy of digital twins. Most currently used systems aim to reduce noise to improve their outputs. Nevertheless, biological systems are characterized by inherent variability, which is necessary for their proper function. The constrained-disorder principle defines living systems as having a disorder as part of their existence and proper operation while kept within dynamic boundaries. In the present paper, we review the role of noise in complex systems and its use in bioengineering. We describe the use of digital twins for medical applications and current methods for dealing with noise and uncertainties in modeling. The paper presents methods to improve the accuracy and effectiveness of digital twin systems by continuously implementing variability signatures while simultaneously reducing unwanted noise in their inputs and outputs. Accounting for the noisy internal and external environments of complex biological systems is necessary for the future design of improved, more accurate digital twins.

Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine

Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.

Building a monitoring matrix for the management of multiple sclerosis

Multiple sclerosis (MS) has a longitudinal and heterogeneous course, with an increasing number of therapy options and associated risk profiles, leading to a constant increase in the number of parameters to be monitored. Even though important clinical and subclinical data are being generated, treating neurologists may not always be able to use them adequately for MS management. In contrast to the monitoring of other diseases in different medical fields, no target-based approach for a standardized monitoring in MS has been established yet. Therefore, there is an urgent need for a standardized and structured monitoring as part of MS management that is adaptive, individualized, agile, and multimodal-integrative. We discuss the development of an MS monitoring matrix which can help facilitate data collection over time from different dimensions and perspectives to optimize the treatment of people with MS (pwMS). In doing so, we show how different measurement tools can combined to enhance MS treatment. We propose to apply the concept of patient pathways to disease and intervention monitoring, not losing track of their interrelation. We also discuss the use of artificial intelligence (AI) to improve the quality of processes, outcomes, and patient safety, as well as personalized and patient-centered care. Patient pathways allow us to track the patient's journey over time and can always change (e.g., when there is a switch in therapy). They therefore may assist us in the continuous improvement of monitoring in an iterative process. Improving the monitoring process means improving the care of pwMS.

Machine Learning in Clinical Trials: A Primer with Applications to Neurology

We reviewed foundational concepts in artificial intelligence (AI) and machine learning (ML) and discussed ways in which these methodologies may be employed to enhance progress in clinical trials and research, with particular attention to applications in the design, conduct, and interpretation of clinical trials for neurologic diseases. We discussed ways in which ML may help to accelerate the pace of subject recruitment, provide realistic simulation of medical interventions, and enhance remote trial administration via novel digital biomarkers and therapeutics. Lastly, we provide a brief overview of the technical, administrative, and regulatory challenges that must be addressed as ML achieves greater integration into clinical trial workflows.

Artificial intelligence-driven drug development against autoimmune diseases

Artificial intelligence (AI)-based predictive models are being used to foster a precision medicine approach to treat complex chronic diseases such as autoimmune and autoinflammatory disorders (AIIDs). In the past few years the first models of systemic lupus erythematosus (SLE), primary Sjögren syndrome (pSS), and rheumatoid arthritis (RA) have been produced by molecular profiling of patients using omic technologies and integrating the data with AI. These advances have confirmed a complex pathophysiology involving multiple proinflammatory pathways and also provide evidence for shared molecular dysregulation across different AIIDs. I discuss how models are used to stratify patients, assess causality in pathophysiology, design drug candidates in silico, and predict drug efficacy in virtual patients. By relating individual patient characteristics to the predicted properties of millions of drug candidates, these models can improve the management of AIIDs through more personalized treatments.

Influence of exercise on quantity and deformability of immune cells in multiple sclerosis

Objective

The study aimed to investigate the effect of exercise on immune cell count and cell mechanical properties in people with multiple sclerosis (pwMS) on different disease-modifying treatments (DMT) vs. healthy controls (HCs).

Methods

A cohort of 16 HCs and 45 pwMS, including patients with lymphopenia (alemtuzumab and fingolimod) as well as increased lymphocyte counts (natalizumab), was evaluated for exercise-mediated effects on immune cell counts and lymphocyte deformability. As exercise paradigms, climbing stairs at normal speed or as fast as possible and cycling were used, while blood samples were collected before, immediately, and 20 as well as 60 min post-exercise. Immune cell subtypes and lymphocyte deformability were analyzed using multicolor flow cytometry and real-time deformability cytometry.

Results

An increase in lymphocytes and selected subsets was observed following exercise in HCs and all pwMS on different DMTs. Patients with lymphopenia exhibited an increase in absolute lymphocyte counts and immune cell subsets till just below or into the reference range. An increase above the upper limit of the reference range was detected in patients on natalizumab. Exercise-induced alterations were observable even in low and more pronounced in high-intensity physical activities. Lymphocyte deformability was found to be only mildly affected by the investigated exercise regimes.

Conclusion

People with multiple sclerosis (PwMS) treated with alemtuzumab, fingolimod, and natalizumab respond to acute exercise with a comparable temporal pattern characterized by the increase of immune cell subsets as HCs. The magnitude of response is influenced by exercise intensity. Exercise-mediated effects should be considered when interpreting laboratory values in patients on immunomodulatory therapy. The impact of exercise on biophysical properties should be further elucidated.

Toward Precision Medicine Using a "Digital Twin" Approach: Modeling the Onset of Disease-Specific Brain Atrophy in Individuals with Multiple Sclerosis

Digital Twin (DT) is a novel concept that may bring a paradigm shift for precision medicine. In this study we demonstrate a DT application for estimating the age of onset of disease-specific brain atrophy in individuals with multiple sclerosis (MS) using brain MRI. We first augmented longitudinal data from a well-fitted spline model derived from a large cross-sectional normal aging data. Then we compared different mixed spline models through both simulated and real-life data and identified the mixed spline model with the best fit. Using the appropriate covariate structure selected from 52 different candidate structures, we augmented the thalamic atrophy trajectory over the lifespan for each individual MS patient and a corresponding hypothetical twin with normal aging. Theoretically, the age at which the brain atrophy trajectory of an MS patient deviates from the trajectory of their hypothetical healthy twin can be considered as the onset of progressive brain tissue loss. With a 10-fold cross validation procedure through 1000 bootstrapping samples, we found the onset age of progressive brain tissue loss was, on average, 5-6 years prior to clinical symptom onset. Our novel approach also discovered two clear patterns of patient clusters: earlier onset vs. simultaneous onset of brain atrophy.

Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit

INTRODUCTION

Digital twins, a form of artificial intelligence, are virtual representations of the physical world. In the past 20 years, digital twins have been utilized to track wind turbines' operations, monitor spacecraft's status, and even create a model of the Earth for climate research. While digital twins hold much promise for the neurocritical care unit, the question remains on how to best establish the rules that govern these models. This model will expand on our group's existing digital twin model for the treatment of sepsis.

METHODS

The authors of this project collaborated to create a Direct Acyclic Graph (DAG) and an initial series of 20 DELPHI statements, each with six accompanying sub-statements that captured the pathophysiology surrounding the management of acute ischemic strokes in the practice of Neurocritical Care (NCC). Agreement from a panel of 18 experts in the field of NCC was collected through a 7-point Likert scale with consensus defined a-priori by ≥ 80% selection of a 6 ("agree") or 7 ("strongly agree"). The endpoint of the study was defined as the completion of three separate rounds of DELPHI consensus. DELPHI statements that had met consensus would not be included in subsequent rounds of DELPHI consensus. The authors refined DELPHI statements that did not reach consensus with the guidance of de-identified expert comments for subsequent rounds of DELPHI. All DELPHI statements that reached consensus by the end of three rounds of DELPHI consensus would go on to be used to inform the construction of the digital twin model.

RESULTS

After the completion of three rounds of DELPHI, 93 (77.5%) statements reached consensus, 11 (9.2%) statements were excluded, and 16 (13.3%) statements did not reach a consensus of the original 120 DELPHI statements.

CONCLUSION

This descriptive study demonstrates the use of the DELPHI process to generate consensus among experts and establish a set of rules for the development of a digital twin model for use in the neurologic ICU. Compared to associative models of AI, which develop rules based on finding associations in datasets, digital twin AI created by the DELPHI process are easily interpretable models based on a current understanding of underlying physiology.

Results of treatment with alemtuzumab in a Spanish cohort of patients with multiple sclerosis in the real world: The RealMS study

Background

Alemtuzumab (ALZ) is a humanized monoclonal antibody approved for the treatment of patients with highly active relapsing-remitting multiple sclerosis (RRMS) administered in two annual courses. The objective of this study was to describe the effectiveness and safety data of ALZ and to report the health resource utilization in patients receiving this treatment.

Methods

In this retrospective, non-interventional study, information was retrieved from patients' medical charts at one center in Spain. Included patients were ≥18 years old, and ALZ treatment was initiated between 1 March 2015 and 31 March 2019, according to routine clinical practice and local labeling.

Results

Of 123 patients, 78% were women. The mean (standard deviation, SD) age of patients at diagnosis was 40.3 (9.1) years, and the mean time since diagnosis was 13.8 (7.3) years. Patients were previously treated with a median (interquartile range; IQR) number of two (2.0-3.0) disease-modifying treatments (DMTs). Patients were treated with ALZ for a mean (SD) of 29.7 (13.8) months. ALZ reduced the annualized relapse rate (ARR) (1.5 before vs. 0.05 after; p < 0.001) and improved the median EDSS (4.63 before vs. 4.00 after; p < 0.001). Most (90.2%) patients were relapse-free while receiving ALZ. The mean number of gadolinium-enhancing [Gd+] T1 lesions was reduced (1.7 before vs. 0.1 after; p < 0.001), and the mean number of T2 hyperintense lesions was maintained (35.7 before vs. 35.4 after; p = 0.392). A total of 27 (21.9%) patients reported 29 autoimmune diseases: hyperthyroidism (12), hypothyroidism (11), idiopathic thrombocytopenic purpura (ITP) (3), alopecia areata (1), chronic urticaria (1), and vitiligo (1). The mean number of health resources (outpatient visits, emergency room visits, hospital admissions, and tests performed in the hospital) used while patients were treated with ALZ progressively decreased from year 1 to year 4, except for a slight increase at year 2 of outpatient visits.

Conclusion

The ReaLMS study provides real-world evidence that ALZ can promote clinical and magnetic resonance imaging disease remission, as well as disability improvement in patients with MS, despite several prior DMT failures. The ALZ safety profile was consistent with data available from clinical trials and other real-world studies. Healthcare resource use was reduced throughout the treatment period.

Lessons from immunotherapies in multiple sclerosis

The improved understanding of multiple sclerosis (MS) neurobiology alongside the development of novel markers of disease will allow precision medicine to be applied to MS patients, bringing the promise of improved care. Combinations of clinical and paraclinical data are currently used for diagnosis and prognosis. The addition of advanced magnetic resonance imaging and biofluid markers has been strongly encouraged, since classifying patients according to the underlying biology will improve monitoring and treatment strategies. For example, silent progression seems to contribute significantly more than relapses to overall disability accumulation, but currently approved treatments for MS act mainly on neuroinflammation and offer only a partial protection against neurodegeneration. Further research, involving traditional and adaptive trial designs, should strive to halt, repair or protect against central nervous system damage. To personalize new treatments, their selectivity, tolerability, ease of administration, and safety must be considered, while to personalize treatment approaches, patient preferences, risk-aversion, and lifestyle must be factored in, and patient feedback used to indicate real-world treatment efficacy. The use of biosensors and machine-learning approaches to integrate biological, anatomical, and physiological parameters will take personalized medicine a step closer toward the patient's virtual twin, in which treatments can be tried before they are applied.

Study on the Applicability of Digital Twins for Home Remote Motor Rehabilitation

The COVID-19 pandemic created the need for telerehabilitation development, while Industry 4.0 brought the key technology. As motor therapy often requires the physical support of a patient's motion, combining robot-aided workouts with remote control is a promising solution. This may be realised with the use of the device's digital twin, so as to give it an immersive operation. This paper presents an extensive overview of this technology's applications within the fields of industry and health. It is followed by the in-depth analysis of needs in rehabilitation based on questionnaire research and bibliography review. As a result of these sections, the original concept of controlling a rehabilitation exoskeleton via its digital twin in the virtual reality is presented. The idea is assessed in terms of benefits and significant challenges regarding its application in real life. The presented aspects prove that it may be potentially used for manual remote kinesiotherapy, combined with the safety systems predicting potentially harmful situations. The concept is universally applicable to rehabilitation robots.

Future directions in regulatory affairs

The field of regulatory affairs deals with the regulatory requirements for marketing authorization of therapeutic products. This field is facing a myriad of forces impacting all aspects of the development, regulation and value proposition of new therapeutic products. Changes in global megatrends, such as geopolitical shifts and the rise of the green economy, have emphasized the importance of manufacturing and supply chain security, and reducing the environmental impacts of product development. Rapid changes due to advances in science, digital disruption, a renewed focus on the centrality of the patient in all stages of therapeutic product development and greater collaboration between national regulatory authorities have been accelerated by the COVID-19 pandemic. This article will discuss the various trends that are impacting the development of new therapies for alleviating disease and how these trends therefore impact on the role of the regulatory affairs professional. We discuss some of the challenges and provide insights for the regulatory professional to remain at the forefront of these trends and prepare for their impacts on their work.

Detecting latent topics and trends of digital twins in healthcare: A structural topic model-based systematic review

Objective

Digital twins (DTs) have received widespread attention recently, providing new ideas and possibilities for future healthcare. This review aims to provide a quantitative review to analyze specific study contents, research focus, and trends of DT in healthcare. Simultaneously, this review intends to expand the connotation of "healthcare" into two directions, namely "Disease treatment" and "Health enhancement" to analyze the content within the "DT + healthcare" field thoroughly.

Methods

A data mining method named Structure Topic Modeling (STM) was used as the analytical tool due to its topic analysis ability and versatility. Google Scholar, Web of Science, and China National Knowledge Infrastructure supplied the material papers in this review.

Results

A total of 94 high-quality papers published between 2018 and 2022 were gathered and categorized into eight topics, collectively covering the transformative impact across a broader spectrum in healthcare. Three main findings have emerged: (1) papers published in healthcare predominantly concentrate on technology development (artificial intelligence, Internet of Things, etc.) and application scenarios(personalized, precise, and real-time health service); (2) the popularity of research topics is influenced by various factors, including policies, COVID-19, and emerging technologies; and (3) the preference for topics is diverse, with a general inclination toward the attribute of "Health enhancement."

Conclusions

This review underscores the significance of real-time capability and accuracy in shaping the future of DT, where algorithms and data transmission methods assume central importance in achieving these goals. Moreover, technological advancements, such as omics and Metaverse, have opened up new possibilities for DT in healthcare. These findings contribute to the existing literature by offering quantitative insights and valuable guidance to keep researchers ahead of the curve.

Cognitive rehabilitation in multiple sclerosis: Three digital ingredients to address current and future priorities

Multiple sclerosis (MS) is a neurological chronic disease with autoimmune demyelinating lesions and one of the most common disability causes in young adults. People with MS (PwMS) experience cognitive impairments (CIs) and clinical evidence shows their presence during all MS stages even in the absence of other symptoms. Cognitive rehabilitation (CR) aims at reducing CI and improving PwMS' awareness of cognitive difficulties faced in their daily living. More defined cognitive profiles, easier treatment access and the need to transfer intervention effects into everyday life activities are aims of utmost relevance for CR in MS. Currently, advanced technologies may pave the way to rethink CR in MS to address the priority of more personalized and effective, accessible and ecological interventions. For this purpose, digital twins, tele-cognitive-rehabilitation and metaverse are the main candidate digital ingredients. Based on scientific evidences, we propose digital twin technology to enhance MS cognitive phenotyping; tele-cognitive-rehabilitation to make feasible the cognitive intervention access to a larger number of PwMS; and metaverse to represent the best choice to train real-world dual- and multi-tasking deficits in virtual daily life environments. Moreover, multi-domain high-frequency big-data collected through tele-cognitive-assessment, tele-cognitive-rehabilitation, and metaverse may be merged to refine artificial intelligence algorithms and obtain increasingly detailed patient's cognitive profile in order to enhance intervention personalization. Here, we present how these digital ingredients and their integration could be crucial to address the current and future needs of CR facilitating the early detection of subtle CI and the delivery of increasingly effective treatments.

Integrating Rehabilomics into the Multi-Omics Approach in the Management of Multiple Sclerosis: The Way for Precision Medicine?

Over recent years, significant improvements have been made in the understanding of (epi)genetics and neuropathophysiological mechanisms driving the different forms of multiple sclerosis (MS). For example, the role and importance of the bidirectional communications between the brain and the gut-also referred to as the gut-brain axis-in the pathogenesis of MS is receiving increasing interest in recent years and is probably one of the most promising areas of research for the management of people with MS. However, despite these important advances, it must be noted that these data are not-yet-used in rehabilitation. Neurorehabilitation is a cornerstone of MS patient management, and there are many techniques available to clinicians and patients, including technology-supported rehabilitation. In this paper, we will discuss how new findings on the gut microbiome could help us to better understand how rehabilitation can improve motor and cognitive functions. We will also see how the data gathered during the rehabilitation can help to get a better diagnosis of the patients. Finally, we will discuss how these new techniques can better guide rehabilitation to lead to precision rehabilitation and ultimately increase the quality of patient care.

What Is a Digital Twin? Experimental Design for a Data-Centric Machine Learning Perspective in Health

The idea of a digital twin has recently gained widespread attention. While, so far, it has been used predominantly for problems in engineering and manufacturing, it is believed that a digital twin also holds great promise for applications in medicine and health. However, a problem that severely hampers progress in these fields is the lack of a solid definition of the concept behind a digital twin that would be directly amenable for such big data-driven fields requiring a statistical data analysis. In this paper, we address this problem. We will see that the term 'digital twin', as used in the literature, is like a Matryoshka doll. For this reason, we unstack the concept via a data-centric machine learning perspective, allowing us to define its main components. As a consequence, we suggest to use the term Digital Twin System instead of digital twin because this highlights its complex interconnected substructure. In addition, we address ethical concerns that result from treatment suggestions for patients based on simulated data and a possible lack of explainability of the underling models.

Digital Twins in Healthcare: Is It the Beginning of a New Era of Evidence-Based Medicine? A Critical Review

Digital Twins (DTs) are used in many different industries (e.g., manufacturing, construction, automotive, and aerospace), and there is an initial trend of applications in healthcare, mainly focusing on precision medicine. If their potential is fully unfolded, DTs will facilitate the as-yet-unrealized potential of connected care and alter the way lifestyle, health, wellness, and chronic disease will be managed in the future. To date, however, due to technical, regulatory and ethical roadblocks, there is no consensus as to what extent DTs in healthcare can introduce revolutionary applications in the next decade. In this review, we present the current applications of DTs covering multiple areas of healthcare (precision medicine, clinical trial design, and hospital operations) to identify the opportunities and the barriers that foster or hinder their larger and faster diffusion. Finally, we discuss the current findings, opportunities and barriers, and provide recommendations to facilitate the continuous development of DTs application in healthcare.

MSProDiscuss™ Clinical Decision Support Tool for Identifying Multiple Sclerosis Progression

This article describes the rationale for the development of the MSProDiscuss™ clinical decision support (CDS) tool, its development, and insights into how it can help neurologists improve care for patients with multiple sclerosis (MS). MS is a progressive disease characterized by heterogeneous symptoms and variable disease course. There is growing consensus that MS exists on a continuum, with overlap between relapsing–remitting and secondary progressive phenotypes. Evidence demonstrates that neuroaxonal loss occurs from the outset, that progression can occur independent of relapse activity, and that continuous underlying pathological processes may not be reflected by inflammatory activity indicative of the patient’s immune response. Early intervention can benefit patients, and there is a need for a tool that assists physicians in rapidly identifying subtle signs of MS progression. MSProDiscuss, developed with physicians and patients, facilitates a structured approach to patient consultations. It analyzes multidimensional data via an algorithm to estimate the likelihood of progression (the MSProDiscuss score), the contribution of various symptoms, and the impact of symptoms on daily living, enabling a more personalized approach to treatment and disease management. Data from CDS tools such as MSProDiscuss offer new insights into disease course and facilitate informed decision-making and a holistic approach to MS patient care.

Timely intervention, monitoring and education MATTERS in MS (TIME MATTERS in MS): Development of a globally applicable quality improvement tool

Background

Previously, consensus MS care standards were defined by MS specialist neurologists from 19 countries. We developed, piloted and refined an Excel-based quality improvement tool to enable MS services to benchmark against these standards. Here, we examine the refined tool.

Objective

To determine the applicability of the quality improvement tool in different healthcare settings.

Methods

MS centres across the globe were invited to pilot the quality improvement tool by coding the medical records of 36 adults with MS. We invited feedback on user friendliness, quality improvement tool usefulness and relevance of data collected.

Results

Seventeen centres from 14 countries participated; 14 completed the post-service evaluation survey. Over 50% of responders rated the tool ‘very easy’ or ‘easy’ to use and ‘very relevant’ to their service. Almost 85% of responders (11/13) planned to introduce changes to their service, including improvements in documentation, communication, interactions with colleagues and referrals; 85% would use a future shorter version of the tool.

Conclusions

The quality improvement tool can enable MS centres globally to benchmark their services. Widespread uptake of a shorter tool may help MS centres to work towards achieving consensus standards for brain health-focused care. Incorporation into routine clinical practice would drive adoption.

Transparent Quality Optimization for Machine Learning-Based Regression in Neurology

The clinical monitoring of walking generates enormous amounts of data that contain extremely valuable information. Therefore, machine learning (ML) has rapidly entered the research arena to analyze and make predictions from large heterogeneous datasets. Such data-driven ML-based applications for various domains become increasingly applicable, and thus their software qualities are taken into focus. This work provides a proof of concept for applying state-of-the-art ML technology to predict the distance travelled of the 2-min walk test, an important neurological measurement which is an indicator of walking endurance. A transparent lean approach was emphasized to optimize the results in an explainable way and simultaneously meet the specified software requirements for a generic approach. It is a general-purpose strategy as a fractional−factorial design benchmark combined with standardized quality metrics based on a minimal technology build and a resulting optimized software prototype. Based on 400 training and 100 validation data, the achieved prediction yielded a relative error of 6.1% distributed over multiple experiments with an optimized configuration. The Adadelta algorithm (LR=0.000814, fModelSpread=5, nModelDepth=6, nepoch=1000) performed as the best model, with 90% of the predictions with an absolute error of <15 m. Factors such as gender, age, disease duration, or use of walking aids showed no effect on the relative error. For multiple sclerosis patients with high walking impairment (EDSS Ambulation Score ≥6), the relative difference was significant (n=30; 24.0%; p<0.050). The results show that it is possible to create a transparently working ML prototype for a given medical use case while meeting certain software qualities.

Digital twins and the ethics of health decision-making concerning children

In this review, we explore from an ethical perspective the opportunities and challenges for decision-making concerning children if digital twins (DTs) were to be used to provide better information about their health status as a basis for proxy decision-making. We note a sense of urgency due to the speed of progress and implementation of this advancing technology and argue that bringing a solid conceptual basis into the development process is of utmost importance for the effective protection of children’s rights and interests.

There is a substantial need in child protection to design the decision-making process in a way that is in the best interests of the child. The solution to this problem will not lie in new technology alone but also in new techniques and technologies that are urgently needed to make children and their interests more visible and to integrate them in decision-making processes. In the health context, this concerns particularly better knowledge of the health status of those children who are especially dependent on the vicarious decisions of others. In doing so, however, we are confronted with an ethical dilemma: on the one hand, children are a particularly vulnerable group, dependent on empowerment and opportunities for genuine participation. In this regard, digital twins (DTs) may provide a substantive opportunity to empower children by providing better and more precise information on their behalf. On the other hand, DT is a technology with great potential to add new forms of vulnerability through its constant, real-time, and ad personam predictions. Consequently, we argue that DTs hold significant potential for a positive contribution to these processes provided that critical concerns regarding vulnerability, recognition, and participation are adequately addressed.