**HDT systems generate virtual twins via deep phenotyping, i.e. the automated processing and integration of decentralized data compiled from patient records, biological data, and mobile sensors**. The physical twin data can be used to measure and then forecast patient response to medication, behavior change, and environmental factors2. The real-time predictive analysis offers new opportunities for timely prevention and treatment, e.g. abdominal aortic aneurysm detection and severity classification4
Potential benefits of precision medicine in cardiovascular disease (CVD) include more accurate phenotyping of individual patients with the same condition or presentation, using multiple clinical, imaging, molecular and other variables to guide diagnosis and treatment. An approach to realising this potential is the digital twin concept, whereby a virtual representation of a patient is constructed and receives real-time updates of a range of data variables in order to predict disease and optimise treatment selection for the real-life patient.
Precision or personalised medicine is an evolving field worldwide and seeks to more accurately phenotype individual patients with the same condition or presentation, allowing tailored screening, diagnostics, and treatment4. Broad application of this concept has been facilitated by biological databases (such as the genome sequence)4 and use of bio- and other markers to stratify patients for more targeted therapy5. For years, ‘omics’ technologies have measured the activities of thousands of genes (transcriptomics), proteins (proteomics) or other molecular features simultaneously from a mixed collection of cells that generate high-dimensional complex data now termed ‘omics’ data, which advance understanding of the genotype-to-phenotype relationship6. The important premise is that genetic, microbial, proteomic, metabolic, clinical, and behavioural pathways characterise patients and their health4. Advanced computational techniques for large data sets may overcome this inherent variability between individuals for more precise clinical decision-making and choice of interventions4. An approach to realising the possibilities of precision medicine is the concept of the digital twin, whereby patient-specific therapy is based on using a virtual replica (the digital twin) to predict treatment outcome and to personalise prognosis for a patient (the real-life twin).
Firstly, deep phenotyping as sourced from electronic health records, biological, clinical, genetic, molecular, and imaging data. Secondly, the phenotyping of real-world data from the person’s environment, using mobile data sensors and wearable devices11. Assimilating these continuously acquired, multi-source data into clinically meaningful knowledge occurs through an automated, iterative process of data pre-processing, data mining, and data integration, that produces more useful information than is provided by any single data source8,12. In the cardiology context, these phenotypic data for a digital twin are analysed in a predictive framework comprising combined statistical and mechanistic modelling that enables reasoning in the twin13. From within the population-based databank, a real-life patient has a digital twin selected that represents the average characteristics of its closest cluster group11. The outcome of virtual interventions subsequently given to the real-life patient then feeds back into the databank to both modify the twin and add to the population data pool11. This dynamic loop is crucial to expanding the databank and ensuring its diverse physiological and demographic make-up.
As intelligent systems that efficiently characterise, understand, cluster and classify complex data, health digital twins are proposed to augment, rather than displace, human intelligence in diagnostic and prognostic decisions in disease8. Estimating and stratifying risks, forecasting progression, choosing an intervention, and predicting its outcome using data streamed and integrated from many sources capture the role for digital twins in realising the possibilities of precision medicine.
For CVD management, the potential for AI systems to more accurately phenotype patients with the same presentation or condition and overcome limitations of current risk-stratification algorithms could enable therapy selection that is based less on the responses of an average person than on the responses predicted in an individualised model
IBM definition: “an environment of linked data and models that reproduce an accurate, virtual representation of real-world entities and processes”
A digital twin is a virtual model designed to accurately reflect a physical object. The object being studied — for example, a wind turbine — is outfitted with various sensors related to vital areas of functionality. These sensors produce data about different aspects of the physical object’s performance, such as energy output, temperature, weather conditions and more. This data is then relayed to a processing system and applied to the digital copy.
Once informed with such data, the virtual model can be used to run simulations, study performance issues and generate possible improvements, all with the goal of generating valuable insights — which can then be applied back to the original physical object.
Three parts: physical product, virtual counterpart, data and information interface between physical space and virtual space
Digital twins vs simulations:
https://www.sciencedirect.com/science/article/pii/S1755581720300110#bib0515