Digital Twin Pain Estimation via Machine Learning Training
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Solution Overview
Problem
Current methods for pain quantification are limited as pain is a subjective observation and existing techniques do not effectively estimate pain that a subject will experience after being treated with a given therapy.
Innovation Solution
The use of digital twins to simulate anatomical structures and systems, allowing for the analysis of digital twin outputs to train machine learning models that estimate pain scores based on applied stimuli.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital twin simulations are used to estimate pain scores, then measurement precision of subjective pain is improved, but device complexity increases
Solution Approach 1:
The patent creates a digital twin - a virtual copy of the patient's anatomical structure - that replicates the physiological behavior and pain responses. This digital replica allows repeated simulations and measurements without affecting the actual patient, enabling precise pain score estimation through multiple virtual experiments while maintaining a manageable model complexity through selective replication of only essential physiological characteristics.
Solution Approach 2:
The digital twin model is trained in advance using historical physiological data and known pain responses to establish baseline relationships between physiological signals and pain scores. This preliminary training phase allows the model to learn complex mappings beforehand, so that during actual use, pain estimation can be performed more efficiently without requiring real-time complex computations.
2Quantity of substance
If multiple digital twin outputs are generated through simulations, then quantity of data for training machine learning models is improved, but loss of time for conducting simulations increases
Solution Approach 1:
The system generates digital twin outputs through periodic simulations that replicate different physiological conditions and stimuli scenarios. By structuring simulations to cover representative cases systematically rather than exhaustively, the model accumulates sufficient training data over time without requiring continuous computational resources, balancing data quantity with time efficiency.
Solution Approach 2:
The digital twin model is designed to be multi-functional, serving both as a training data generator and as a predictive tool for clinical decision-making. By creating a versatile model that can handle various types of physiological simulations and predict multiple outcomes, the system maximizes the utility of each simulation run, reducing the total number of simulations needed while still generating comprehensive training datasets.
Data Source
AI summary
Embodiments are described herein for training machine learning models using digital twin outputs, including for purposes of pain quantification and estimation. In various embodiments, simulation parameter(s) may be applied to a digital twin created for a subject. The digital twin may simulate an anatomical structure of the subject, and the simulation parameter(s) may cause the digital twin to generate digital twin output that simulates behavior of the anatomical structure in response to the one or more simulation parameters. Data indicative of the digital twin output may be applied as input across a pain estimation machine learning model to generate pain estimation output. Based on the pain estimation output, a quantification of pain, e.g., an estimated pain score, may be rendered using an output device and/or used to select a treatment plan.