Digital twins and synthetic data are both advanced tools used in data-driven fields like healthcare, engineering, and manufacturing, but they serve different purposes and operate in distinct ways. Here's a breakdown of their differences:
Definition
Digital Twins: A digital twin is a real-time, virtual representation of a physical object, process, or system. It mirrors its real-world counterpart using data streams from sensors or other input sources, continuously updating to reflect real-time changes.
Synthetic Data: Synthetic data is artificially generated data that mimics the statistical properties of real data but is not tied to any actual individual or real-world entity. It is created through algorithms, simulations, or models to replicate realistic data for training, testing, or analysis purposes.
Purpose
Digital Twins: The primary purpose of a digital twin is to monitor, simulate, and optimize real-world systems in real time. It allows users to observe, analyze, and predict performance, failures, and inefficiencies of a system or object without needing to interact directly with the physical counterpart.
Synthetic Data: Synthetic data is used primarily for data-driven tasks like machine learning model training, testing algorithms, or conducting analyses where real data is scarce, sensitive (e.g., medical or financial data), or not accessible. Its purpose is to provide a usable, privacy-safe, and scalable data set that approximates real-world scenarios.
Data Sources
Digital Twins: The data used in digital twins comes from real-world sensors, devices, or systems that feed real-time information into the virtual model. The twin continuously updates to stay in sync with its physical counterpart.
Synthetic Data: Synthetic data is not based on real-time inputs from physical objects but is generated using statistical methods, simulations, or generative models that can mimic the characteristics of real-world data.
Areas of Application
Digital Twins:
- Manufacturing: Monitoring and optimizing factory operations.
- Healthcare: Personalized medicine, patient care simulations, equipment management.
- Smart Cities: Real-time tracking and simulation of infrastructure like traffic and energy grids.
- Aerospace: Simulation of aircraft systems for predictive maintenance and performance optimization.
Synthetic Data:
- Artificial Intelligence (AI) & Machine Learning (ML): Training models without exposing sensitive data.
- Testing and Validation: Providing data for software and system testing.
- Healthcare: Creating patient data that preserves privacy for research and AI development.
- Finance: Simulating transaction data for risk modeling or fraud detection without disclosing real client information.
Realism and Accuracy
Digital Twins: Since digital twins are continuously updated with real-world data, they offer highly accurate, real-time reflections of the actual system they represent. This makes them effective for operational monitoring and decision-making.
Synthetic Data: While synthetic data can mimic real data closely, its accuracy depends on how well it is generated and modeled. It is not tied to a specific real-world entity and may not reflect real-time dynamics but can offer realistic approximations for model training and analysis.
Interactivity
Digital Twins: Digital twins allow for interaction, simulation, and real-time manipulation of the virtual model. Users can test scenarios, predict future behavior, and optimize performance before making changes to the physical system.
Synthetic Data: Synthetic data is static and doesn’t provide real-time feedback. It is typically used in batch processes like training machine learning models, and its interactivity depends on how it is applied within simulations or other environments.
Privacy and Security
Digital Twins: Since digital twins involve real-world data, there are potential concerns about data privacy and security, especially if they are used in sensitive areas like healthcare or critical infrastructure.
Synthetic Data: Synthetic data inherently avoids privacy issues because it doesn’t represent any real individuals or entities. It is often used to generate privacy-preserving data for ML and research purposes.
Scalability
Digital Twins: The scalability of a digital twin depends on the complexity of the physical system being modeled. It may require large amounts of data, processing power, and sophisticated infrastructure to keep the digital twin in sync with its physical counterpart.
Synthetic Data: Synthetic data is highly scalable since it can be generated in large quantities relatively easily and doesn’t depend on real-world constraints. This makes it ideal for machine learning applications where vast amounts of data are needed.
Example of applications:
Digital Twins: Cukic et al. 2024 developed and tested a transdermal fentanyl digital twin (DT) dosing tool in patients with cancer with chronic pain to optimise individual fentanyl dosing in patients with advanced cancer switching from oral or intravenous opioids to transdermal fentanyl by using a physics-based DT that is fed by important clinical and physiological parameters. “Individual tailoring of transdermal fentanyl therapy is an approach with the potential for personalised and effective care with an improved benefit-risk ratio. However, clinical validation of physics-based digital twins (PBDT) dosing is crucial to proving clinical benefit”. - https://pubmed.ncbi.nlm.nih.gov/39317494/
Synthetic Data:
Zhang et al 2024 proposed a system introducing a novel approach, Generative Adversarial Networks Augmented Naïve Bayes (GAN-ANB), to classify high-risk Coronary Artery Disease patients using Coronary Computed Tomography Angiography (CCTA) imaging data. The database used included images CCTA records of 5,000 individuals and was used to generate synthetic patient profiles, and a discriminator to distinguish between genuine and synthetic profiles to improve the identification of high-risk CAD patients - https://pubmed.ncbi.nlm.nih.gov/39375407/
References:
Cukic M, Annaheim S, Bahrami F, Defraeye T, De Nys K, Jörger M. Is personal physiology-based rapid prediction digital twin for minimal effective fentanyl dose better than standard practice: a pilot study protocol. BMJ Open. 2024 Sep 24;14(9):e085296. doi: 10.1136/bmjopen-2024-085296. PMID: 39317494; PMCID: PMC11423737.
Zhang L, Haldorai A, Naik N. GAN-Augmented Naïve Bayes for identifying high-risk coronary artery disease patients using CT angiography data. Sci Rep. 2024 Oct 7;14(1):23278. doi: 10.1038/s41598-024-73176-3. PMID: 39375407; PMCID: PMC11458606.