Koninklijke Philips Corporation N.v.

Lieke Gertruda Elisabeth Cox

Erik Gosuinus Petrus Schuijers

Cornelis Petrus Hendriks

Murtaza Bulut

Valentina Lavezzo

Eduard Gerard Marie Pelssers

Raymond Van Ee

Hernán Guillermo Morales Varela

Bjorn Stemkens

A method of developing a personalized digital model of an anatomy of a person by a computer system comprising a processor arrangement and a communication module. The processor arrangement receives input data on an actual physical condition of the person from the communication module; searches a database of digital models modelling different physical conditions and selects a digital model from said database that closely matches the actual physical condition of the person based on some received input data; personalizes the selected digital model by developing the modelled physical condition of the selected digital model with a physiological development model based on received input data such that the developed modelled physical condition closely resembles said actual physical condition; and generates an output of the personalized digital model of said person on a data storage medium. Also disclosed, a computer program product and computer system for implementing the method.

Digital twin of a person

A system for predicting the onset of a physiological condition of a subject combines an in-vitro subject model having input parameters relating to actual or predicted external stimuli to which the subject is or may be exposed and an in-silico model. The in-vitro model outputs subject-specific parameters which are not being monitored, or cannot be monitored, in-vivo. The in-silico subject model uses subject-specific parameters to predict the response of the subject to the actual or predicted external stimuli and thereby predict an onset of a physiological condition of the subject.

Predicting the onset of a physiological condition of a subject using a digital biophysical model

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.

Quantification and estimation based on digital twin output

A computer system is disclosed comprising a processor arrangement communicatively coupled to a data storage arrangement storing a digital model of a section of a lumen system of a patient; and a communication module communicatively coupled to said processor arrangement and arranged to receive sensor data pertaining to an internal parameter of said lumen system from a sensor within said section of the lumen system. The processor arrangement is arranged to receive said sensor data from the communication module; retrieve said digital model from the data storage arrangement; simulate an actual physical condition of said lumen system by developing said digital model based on the received sensor data; and generate an output relating to said simulated actual physical condition for updating an electronic device. Also disclosed is a method of monitoring a patient with such a computer system, a computer program product for implementing such a method and a patient monitoring system.

Patient lumen system monitoring

Disclosed is a method () of providing a personalized parameter model of at least part of the physiology of a person () carrying a set of sensors () with a computer system () comprising a processor arrangement () for the purpose of updating a biophysical model or digital twin of the person. The method comprises, with said processor arrangement, receiving () sensor data from said set of sensors with the communication interface over a defined period of time, the sensor data from each sensor representing a parameter relevant to an actual physiological state of the person; evaluating () the received sensor data from each sensor to define the personalized parameter model (), said model including the evaluated dynamic behaviour of the values of each parameter observed with said sensors over the defined period of time and providing () the defined personalized parameter model for updating a digital model () of at least part of the anatomy of said person. Also disclosed is a method () for updating such a digital twin in which the digital twin is updated using such a personalized parameter model. In addition, a computer program product for implementing such methods and a computer system () implementing such methods are also disclosed.

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Eindhoven, Netherlands

Lieke Gertruda Elisabeth Cox