An exciting new technology trend that could revolutionize healthcare is the “digital twin”. Digital twin simply explained is a digital image of a physical object.
In healthcare, a Digital Twin can be defined in relation to a patient as one or more computational models that represent a dynamic digital representation of a real biological target ( “artifact”) or aspect of a person’s physical condition.
What are the advantages of using a Digital Twin?
This technology has been established in manufacturing and industry for decades and is used to model and simulate real-world assets such as a machine or even an entire factory before they are built. These Digital Twins are used to predict failures, plan maintenance strategies, or even control operations in a changing environment. The goal is to computationally model these systems to develop and test them faster and cheaper compared to real-world environments. Digital Twins can thus be used to correct potential faults before they occur.
The goal of Digital Twin models is to provide actionable insights and decision support across the healthcare system. By providing high-quality precision care, Digital Twin technology will enable massive personalization of disease prevention, diagnosis, treatment, and monitoring.
What is a Digital Twin used for in medicine?
Personalized medicine requires the integration and processing of large amounts of data. Digital Twins, i.e. high-resolution models of individual patients, enable, among other things:
- proactive health management for patients
- the optimization of therapies
- enable the prediction of treatment outcomes, and much more.
The vision: in the future, for example, surgeries can always be planned and performed in advance on a computer, or a drug delivery can be tested out digitally.
Patients will thus always receive the medication that had the best effect on their digital twin or a treatment adapted to their anatomy.
This allows therapies to be optimized, risks during operations to be avoided and costs for unnecessary interventions to be saved.
While a comprehensive digital model of a whole person is not yet a reality, various Digital Twin models have already been developed and tested. These include simulations of specific body parts or organs (e.g., heart or lungs), models of disease states (e.g., lung cancer), or simplified “avatar” representations of a person’s physical body. Digital Twins are typically created to provide decision support for specific clinical questions. The models can be used to simulate a variety of scenarios in silico, virtually test therapies or medical devices, and predict trends and outcomes.
One Digital Twin example is docq RPE. This is a medical software for surgical planning of a so-called forced palatal expansion. Based on the patient’s individual anatomy, it calculates the optimal incision pattern to ensure a symmetrical face after forced widening of the upper jaw.
A key feature of Digital Twins is that they can be dynamically controlled by the user and scenarios can be simulated to explore “what if” questions. The models can also be dynamically updated and evolve as new clinical knowledge and data become available. Digital Twins can be used in all clinical phases, from disease prevention to diagnosis and therapy to monitoring and home care. Because Digital Twins can be individualized with the patient’s data, they provide personalized decision support and enable precision medicine approaches that are best suited for each patient.
As mentioned earlier, it is currently not possible to create a Digital Twin for a entire human being. There are several reasons for this. For example, imaging human organs is very complex. The imaging of tissue and soft tissues is also more complex than the imaging of solid objects such as bones. This is because the parameters of solid materials are predictable and have fixed properties. However, this is not the case for soft tissue in humans and can vary greatly.
How is a Digital Twin created and how does it work?
A Digital Twin is a dynamic software model that captures metadata. Obtaining the appropriate data is one of the most important requirements in creating a Digital Twin. The data comes from various sources such as sensor-based systems, including imaging scanners, laboratory tests, wearable monitoring devices, or even observations. In addition, the Digital Twin can be fed with data from empirical or experimental sources, such as historical patient data that is collected or from clinical and scientific observations.
Since data is often in unstructured form, it must first be converted into a structured, or machine readable, form before it can be processed and used in Digital Twin models. Computer models created from the data can vary in scope and complexity, from predictive algorithms using a limited number of data points to full organ models with multiple levels of simulation.
Ideally, the model is constantly updated with current data.
Model usage can also create a feedback loop that can dynamically update and improve the model over time.
The Digital Twin thus responds to changes. From this, it can be derived how targeted improvements can be achieved.
Challenges for the implementation of Digital Twins
To get approval from regulatory agencies, validation is an essential step in creating a Digital Twin. To do this, regulators have begun drafting guidelines for AI-based medical devices worldwide, Digital Twins included.
So, once a Digital Twin is created for clinical use, it is validated with additional data, retrospective or prospective, to ensure it works.
Once a Digital Twin has been created, the model validated and approved by regulatory authorities, it can be made available to physicians for treatment.
To do this, a digital infrastructure must be created that enables the creation, storage, transmission, analysis, visualization and reporting of the models and data.
In addition, a connection must be made to devices or other storage media such as electronic medical records, PACS, RIS, LIS, in which the health data required for the models are stored.
Access to this data is based on standards and interoperability, which are still under development. In addition, the infrastructure must also provide a sufficient level of security and privacy protection, as health data can contain very sensitive information with limited access. Here, it is important to factor in regional conditions and requirements for the protection of personal data. In the United States, this includes the Health Insurance Portability and Accountability Act (HIPAA) regulations, and in Europe, the General Data Protection Regulation (GDPR).
The increasing need for clinical data has raised numerous important issues related to data ownership and governance that could impact the adoption of Digital Twin models. These include the legal framework and laws that govern patient data privacy and confidentiality, as well as the security required. The issue of data ownership is very complicated.
Current regulations vary widely by location and are considered insufficient by many experts. Some believe it is more an issue of control, privacy, and permission, as well as value and purpose, rather than ownership.
What else should I know about Digital Twins?
Open and decentralized alternatives with secure platforms and data access points maintained by partner networks are becoming increasingly popular. In addition, new models for managing health data are planned for the coming years, with individuals having control over ownership and access to their own data. Customer demands will drive the advancement and use of Digital Twin models, while technological improvements will support this. In the near future, Digital Twin models will be developed for a limited number of high-value use cases, adding a new level of “intelligence” to existing healthcare products and services.
In the future, Digital Twin models may evolve into a modular, potentially networked system of models with advanced capabilities. This will support a broader range of use cases and the creation of new products and services. This is enabled by a high level of interconnectivity due to extensive digitization and the availability of healthcare data.
Simq supports you with the implementation
The ability to simulate human physiology in silico with Digital Twin models represents a new paradigm for using a patient’s available digitized health data. These models can be used to derive personalized, actionable insights that can, in turn, lead to optimized clinical processes and improved outcomes. Simq, as a solution partner, specializes in integrating this technology into future medical software products for healthcare companies to drive the personalization of healthcare. Medical device manufacturers thus gain easy access to medical simulation and healthcare innovation of the future with Simq’s technology and expertise.