Digital Twin Simulation for Patient-Specific Treatment Evaluation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Clinical trials for new medical treatments are costly and pose risks due to the need for live patient testing, necessitating a more efficient and safer evaluation method.
Innovation Solution
Simulation-based evaluation systems and methods using patient-specific anatomical and physiological models to simulate treatment effects across patient populations, allowing for digital clinical trials that compare outcomes between experimental and control groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If clinical trials are conducted on live patients to assess treatment safety and efficacy, then reliable medical evaluation data is obtained, but high costs and risks to patients are incurred
Solution Approach 1:
The patent creates virtual copies of patients using digital twins that replicate physiological characteristics, anatomical structures, and disease states. These digital patient models allow treatment evaluation without exposing real patients to risks. The virtual patient population is generated by collecting and processing real patient data to create accurate digital representations that can be used for simulated clinical trials.
Solution Approach 2:
The system performs preliminary treatment evaluation on virtual patient populations before actual clinical trials are conducted. By simulating treatment effects on digital twins first, the system identifies potential safety issues and efficacy patterns in advance, allowing researchers to refine treatment protocols and select the most promising candidates for real-world testing, thereby reducing risks to actual patients.
2Reliability
If clinical trials are conducted on live patients to assess treatment safety and efficacy, then reliable medical evaluation data is obtained, but high costs are incurred
Solution Approach 1:
The patent creates virtual copies of patients using digital twins that replicate physiological characteristics, anatomical structures, and disease states. These digital patient models allow treatment evaluation without exposing real patients to risks. The virtual patient population is generated by collecting and processing real patient data to create accurate digital representations that can be used for simulated clinical trials.
Solution Approach 2:
The system performs preliminary treatment evaluation on virtual patient populations before actual clinical trials are conducted. By simulating treatment effects on digital twins first, the system identifies potential safety issues and efficacy patterns in advance, allowing researchers to refine treatment protocols and select the most promising candidates for real-world testing, thereby reducing risks to actual patients.
3Object-affected harmful factors
If digital simulation is used to evaluate treatments before clinical trials, then patient risks and costs are reduced, but the complexity of creating and managing patient-specific models increases
Solution Approach 1:
The patent divides the complex task of patient-specific modeling into modular components: data acquisition modules that collect patient information, processing modules that transform data into digital representations, and simulation modules that execute treatment evaluations. This segmentation allows each component to be developed and validated independently, reducing overall system complexity while maintaining comprehensive functionality.
Solution Approach 2:
The system employs universal digital twin frameworks that can represent multiple patient types and disease states using common underlying models. Rather than creating entirely separate models for each patient, the system uses parameterized templates that can be customized for different individuals, reducing the complexity of model creation and management while maintaining patient-specific accuracy.
Data Source
AI summary
Methods and systems may be used for performing computer-simulated evaluation of treatments. For example, the method may include: for each of a plurality of patients, receiving one or more patient-specific anatomical and/or physiological models; selecting, from the plurality of patients, a set of patients that have one or more common characteristics; for each patient in an experimental group, modifying at least one model of the respective one or more patient-specific models to obtain at least one modified patient-specific model that models an effect of an evaluation treatment on the respective patient, and calculating a value of an evaluation endpoint based on the respective at least one modified patient-specific model; and comparing the calculated values of the evaluation endpoint with one or more control values of the evaluation endpoint for patients that satisfy the one or more selection criterion.