Digital Twins & In Silico Trials
Siemens Healthineers Digital Twin
by Siemens Healthineers AG
Patient-specific cardiac digital twins enabling precision planning for structural heart interventions
Category
Digital Twins & In Silico Trials
Founded
2018
Headquarters
Erlangen, Germany
Overview
Siemens Healthineers has developed a portfolio of digital twin technologies for cardiovascular medicine, most notably patient-specific cardiac simulation models used to plan structural heart interventions such as transcatheter aortic valve replacement (TAVR), left atrial appendage occlusion, and mitral valve repair. These digital twins are constructed from CT and MRI imaging data using AI-powered segmentation and finite element modeling, enabling interventional cardiologists to virtually test device sizing, deployment angles, and potential complications before the procedure. The HeartNavigator and syngo.via software platforms integrate digital twin planning into clinical imaging workflows. Interventional cardiologists and cardiac surgeons at major heart centers worldwide use Siemens Healthineers digital twin tools to reduce procedural complications, optimize device selection, and train structural heart teams on complex cases. Clinical studies have demonstrated that TAVR planning using patient-specific simulation reduces paravalvular leak rates and improves device sizing accuracy. Siemens Healthineers has embedded digital twin capabilities into its broader imaging and diagnostics ecosystem, connecting simulation with real-time imaging guidance during procedures. Siemens Healthineers' strength in digital twins stems from its unique position as both an imaging equipment manufacturer and a software/AI company: access to the world's largest imaging dataset, combined with deep clinical relationships at major heart centers, enables training and validation of models that purely software companies cannot replicate. The company's digital twin approach is tightly integrated with its cardiovascular imaging products — SOMATOM CT scanners, MAGNETOM MRI, and ACUSON ultrasound — creating a closed-loop workflow from patient imaging to simulation to guided intervention that competing standalone software platforms cannot match.
Key Features
Trial Design Optimization
AI-optimized trial design including dosing schedules, endpoints, and patient stratification.
Regulatory Evidence Generation
Generate computational evidence packages aligned with FDA guidance for regulatory submissions.
Real-World Data Integration
Calibrate and validate models using real-world clinical data from healthcare systems.
Virtual Patient Modeling
Create digital patient models simulating drug responses across diverse population demographics.
In Silico Clinical Trials
Virtual clinical trials reduce time and cost of traditional Phase I-III studies by 30-50%.
Pros & Cons
Pros
- +Virtual patient models simulate drug responses across diverse population demographics
- +In silico clinical trials reduce time and cost of traditional Phase I-III studies by 30-50%
- +Multi-scale modeling connects molecular interactions to organ-level physiological responses
- +Regulatory acceptance growing with FDA guidance on computational modeling for device and drug evaluation
- +Synthetic control arms reduce the need for placebo groups in rare disease clinical trials
- +Predictive toxicology models identify safety liabilities before first-in-human dosing
- +Integration with real-world clinical data improves model calibration and prediction accuracy
Cons
- −Model validation against real clinical data is essential but time-consuming and expensive
- −Regulatory acceptance of in silico evidence varies across jurisdictions and therapeutic areas
- −Computational models cannot fully capture the complexity of human biological variability
- −Requires extensive clinical data for initial model calibration and ongoing validation
Use Cases
Research Workflow Optimization
AI-powered optimization of research workflows to accelerate discovery timelines and improve reproducibility.
Data Analysis & Insights
Machine learning analysis of complex biological datasets to extract actionable insights and identify patterns.
Collaboration & Knowledge Management
Platform-enabled collaboration across distributed research teams with integrated data sharing and knowledge capture.