Digital Twins and the Emerging Science of Self: Implications for Digital Health Experience Design and “Small” Data

Schwartz, Steven M.; Wildenhaus, Kevin; Bucher, Amy; Byrd, Brigid

doi:10.3389/fcomp.2020.00031

Cited by 76 publications

(52 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Schwartz et al present sone important design considerations for digital twins in medicine and healthcare, including, among others, clear data visualisation, ease of access and accessibility, ease of adding and removing data sources (e.g., data removal due to privacy breaches or known errors in the data), and integration into clinical workflow [ 36 ].…”

Section: Issues and Challenges In Human Digital Twinsmentioning

confidence: 99%

Digital Twins: From Personalised Medicine to Precision Public Health

Boulos

Zhang

2021

JPM

245

View full text Add to dashboard Cite

A digital twin is a virtual model of a physical entity, with dynamic, bi-directional links between the physical entity and its corresponding twin in the digital domain. Digital twins are increasingly used today in different industry sectors. Applied to medicine and public health, digital twin technology can drive a much-needed radical transformation of traditional electronic health/medical records (focusing on individuals) and their aggregates (covering populations) to make them ready for a new era of precision (and accuracy) medicine and public health. Digital twins enable learning and discovering new knowledge, new hypothesis generation and testing, and in silico experiments and comparisons. They are poised to play a key role in formulating highly personalised treatments and interventions in the future. This paper provides an overview of the technology’s history and main concepts. A number of application examples of digital twins for personalised medicine, public health, and smart healthy cities are presented, followed by a brief discussion of the key technical and other challenges involved in such applications, including ethical issues that arise when digital twins are applied to model humans.

show abstract

Section: Issues and Challenges In Human Digital Twinsmentioning

confidence: 99%

Digital Twins: From Personalised Medicine to Precision Public Health

Boulos

Zhang

2021

JPM

245

View full text Add to dashboard Cite

show abstract

“…In particular, biomechanical simulations and motion data models are being used to create the personalized "digital twins." This concept refers to the digital representation of the patient based on their profile health (Schwartz et al, 2020), which allows to simulate the different types of the biomarkers through this model, making the predictions and simulations of the evolution of the patient (Voigt et al, 2021) and testing and evaluating the wearable robotic systems before deploying the physical prototypes .…”

Section: Creating the Computational Neurorehabilitation Models For The Patient-tailored Therapiesmentioning

confidence: 99%

“…The concept of the patient-tailored therapies by using the computational neurorehabilitation is currently exploring the development of the new biomarkers from three main perspectives: (1) a neuroscience perspective (i.e., developing the mathematical models of the mechanisms of the activity-dependent plasticity; Reinkensmeyer et al, 2016 ), (2) a clinical perspective in which the clinically relevant biomarkers are being identified and used to create the algorithms for decision-making (i.e., prescribing the individualized intensities of the rehabilitation; Jeffers et al, 2018 ), and (3) a personalized biomechanical and sensor perspective in which the biomarkers are being used to complement the human movement analysis and wearable system design (Derungs and Amft, 2020 ). In particular, biomechanical simulations and motion data models are being used to create the personalized “digital twins.” This concept refers to the digital representation of the patient based on their profile health (Schwartz et al, 2020 ), which allows to simulate the different types of the biomarkers through this model, making the predictions and simulations of the evolution of the patient (Voigt et al, 2021 ) and testing and evaluating the wearable robotic systems before deploying the physical prototypes (Derungs and Amft, 2020 ).…”

Section: Latest Trends and Perspectives In The Fieldmentioning

confidence: 99%

Neuromechanical Biomarkers for Robotic Neurorehabilitation

et al. 2021

View full text Add to dashboard Cite

One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the “biomarkers” that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the “Rehabilomics” has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective.

show abstract

“…Lately, a new technology has been advocated that potentially can solve both these problems: physiologically based digital twins (Fig 1A) (Shamanna et al, 2020, Corral-Acero et al, 2020, Golse et al, 2021, Schwartz et al, 2020. A physiologically based digital twin is a personalized computer model that describes the underlying physiology in a specific person or patient (Fig 1B).…”

Section: Introductionmentioning

confidence: 99%

Digital twin predicting diet response before and after long-term fasting

Arrestam

Simonsson

Ekstedt

et al. 2021

Preprint

View full text Add to dashboard Cite

SummaryToday, there is great interest in diets proposing new combinations of macronutrient compositions and fasting schedules. Unfortunately, there is little consensus regarding the impact of these different diets, since available studies measure different sets of variables in different populations, thus only providing partial, non-connected insights. We lack an approach for integrating all such partial insights into a useful and interconnected big picture. Herein, we present such an integrating tool. The tool uses a novel mathematical model that describes mechanisms regulating diet-response and fasting metabolic fluxes, both for organ-organ crosstalk, and inside the liver. The tool can mechanistically explain and integrate data from several clinical studies, and correctly predict new independent data, including data from a new clinical study. Using this model, we can predict non-measured variables, e.g. hepatic glycogen and gluconeogenesis, and we can quantify personalized expected differences in outcome for any diet. This constitutes a new digital twin technology.

show abstract

Digital Twins and the Emerging Science of Self: Implications for Digital Health Experience Design and “Small” Data

Cited by 76 publications

References 55 publications

Digital Twins: From Personalised Medicine to Precision Public Health

Digital Twins: From Personalised Medicine to Precision Public Health

Neuromechanical Biomarkers for Robotic Neurorehabilitation

Digital twin predicting diet response before and after long-term fasting

Contact Info

Product

Resources

About