US 2021/0241116 A1 · QUANTIFICATION AND ESTIMATION BASED ON DIGITAL TWIN OUTPUT

Abstract

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.

Claims15

Dependent claims

A method implemented using one or more processors, comprising:

applying one or more simulation parameters to a digital twin created for a subject,
wherein the digital twin simulates an anatomical structure of the subject, and
wherein the one or more simulation parameters 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;
applying data indicative of the digital twin output as input across a pain estimation machine learning model to generate pain estimation output,
wherein the pain estimation machine learning model is trained to estimate a quantification of pain that the subject would experience based on application of the simulation parameters, and
wherein the pain estimation output indicates the quantification of pain;
and
based on the pain estimation output, rendering the quantification of pain using an output device or selecting a treatment plan.

5 more elements

A method of training a pain estimation machine learning model, the method implemented using one or more processors and comprising:

providing a first set of digital twins to simulate an anatomical structure in a plurality of pained subjects;
determining pain scores observed by the plurality of pained subjects;
applying one or more simulation parameters to each digital twin of the first set to generate a plurality of digital twin outputs, wherein each digital twin output simulates behavior of the anatomical structure in a respective subject of the first set in response to the one or more simulation parameters;
applying data indicative of the plurality of digital twin outputs as inputs across a pain estimation machine learning model to generate pain estimation outputs;
and
training the machine learning model based on comparison of the pain estimation outputs to the pain scores.

3 more elements

Dependencies are inferred from the claim text.

Quantification and estimation based on digital twin output

Life of this patent

Quantification and estimation based on digital twin output

Life of this patent

Abstract

Claims15