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1. Digital Twins: whole person, mental health

MSM lead: Gary An; IMAG lead: Liz Ginexi

Proposed Working Definition of Medical Digital Twin

The term "medical digital twin" is currently used with multiple definitions. For purposes of this breakout session, we would like to provide a working definition tailored to Multiscale Modeling and uses the minimally sufficient criteria for a "digital twin" as described by Grieves in 2019 for industrial applications (recognizing that functionally this process had been in use for several years at NASA). These criteria are essentially:

1. A data structure for the real-world object/system that would allow a specific real world object/system to be represented by a personalized/individualized virtual object or ensemble of object. (Twin structure)
2. Representation of some process that links time points in the virtual object (Twin behavior)
3. A link between the real and virtual twins that provides an ongoing data stream to update the virtual object (Twin Connectivity)

Operational Classification of Medical Digital Twins based on intended use:

1. Monitoring/Diagnosis/Prediction/Forecasting
2. Optimization of existing therapies
3. Development/testing of novel therapies

Because this is a meeting of the "Multiscale Modeling Consortium" we would like to emphasize discussions regarding Medical Digital Twins that utilize/require mechanism-based multi-scale models

Some Pre-emptive Answers to Commonly Asked Questions:

• Q1: What is the difference between a model and a Digital Twin?
• A1: Most would say that the difference is that a Digital Twin includes a model (#1 and #2 from above) with a data stream from the real world twin that updates the virtual twin (#3 above). The update-ability of the Digital Twin is a key point. See Ref https://amses-journal.springeropen.com/articles/10.1186/s40323-020-0014….
• Q2: What is the difference between virtual populations and populations of digital twins"
• A2: We would suggest that the difference is the personalization capability of a digital twin. While methods for generating virtual populations can theoretically encompass the heterogeneity present across a population, there is generally no explicit goal of representing a specific individual. This does not mean that methods for creating virtual populations cannot be used to create digital twins, but rather that an application may not be explicitly specified to do so.
• Q3: What is the difference between a personalized predictive model and a digital twin?
• A3: While this may be a potential point of disagreement, one could point again to Criteria #3 above to make the distinction: with a Digital Twin, there must be recurrent and ongoing data feedback from the real world to update and refine the future behavior of the virtual twin. For example, personalized predictive models, such as ones that utilize genomic profiles of tumors to suggest therapies specific for those tumors, while incredibly useful, only become digital twins if the tumor/patient's response can be fed back into the virtual object such that future behavior of the real world twin can be projected/forecast. We would hope that rather than being a point of contention, this distinction would be a starting point for discussion about desirable future capabilities that can turn personalized predictive models into digital twins.
• Q4: What is the difference between virtual tissues and a medical digital twin?
• A4: Just as an individual is made up of multiple tissues, so too should our aspirational medical digital twin be made up of multiple computational representations of its tissues. While the eventual goal would be an integrated virtual representation of a complete person, we recognize that in the path towards this aspirational goal it will be necessary to identify intermediate points where the utility of the digital twin concept (specifically the ability to utilize ongoing data links between the real and digital world) can be demonstrated, and that these intermediate points will involve the representation of specific disease processes that employ a subset of integrated virtual tissues. As with our pre-emptive answer to Q3/A3 above, rather than becoming a point of semantic contention, we hope that the perspective that digital twins represent integration of multiple virtual tissues will provide a starting point for discussions related to the modularity and composability challenges present as open research questions.

Agenda/Discussion Topics for this Breakout Session

• How would the MSM Consortium play a role?

• More specifically, where can the MSM Consortium fill gaps in existing large-scale initiatives regarding the development of medical digital twins (such as the European projects: the European Virtual Twin/EDITH project https://www.edith-csa.eu/ and the Neurotwin project https://www.neurotwin.eu/)?

• How to integrate mechanism-based multi-scale modeling with machine learning/artificial intelligence to develop medical digital twins?

• How to foster collaborations that integrate underlying digital twin model development (Criteria #2) with developers of sensor/assay technologies needed to fulfill Criteria #3?

• Propose strategies for being able to capture inter-individual heterogeneity in a way that allows personalization of digital twins.

• Propose strategies for dealing with uncertainty and parameterization when integrating models/modules that cross multiple scales.

• Identify strategies to find useful "intermediate points" for medical digital twin applications while still moving towards a larger vision of an integrated, whole-body medical digital twin.

Resources:

Digital Twins resources