Abstract: 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.

Abstract: Provided herein are systems and methods of predicting and managing blood glucose levels in individuals, including systems and methods of predicting blood glucose levels based on predicted future glucose factors. Also provided herein are systems and methods of recommending glucose interventions based thereon. It is appreciated by the present disclosure that it is better to prevent extreme blood glucose levels before they occur than merely detecting such levels when they occur. Accordingly, the systems and methods described herein utilized a combination of contextual information and current time glucose measurements/estimates to predict the likelihood of different scenarios that might lead to such extreme levels before they occur.

Abstract: The present invention relates to a method, device and system for assessing reliability of an estimated metric relating to a physiological state of a user. The method comprises obtaining one or more sensor signals relating to the physiological state of the user, estimating independently from each other a first metric and a second metric using a different method and/or algorithm based on the one or more sensor signals, wherein the first metric is related to the second metric by a first mathematical function, determining a first value of the first mathematical function, comparing said first value to a bound of the first value, assessing a reliability rating for the estimation of the first metric and/or the second metric based on said comparison, and outputting the assessed reliability rating.

Abstract: 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.

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.

Abstract: Hair shine compositions are disclosed. In embodiments, a hair shine composition may include greater than or equal to 50 wt/wt % and less than or equal to 95 wt/wt % water; greater than or equal to 0.5 wt/wt % and less than or equal to 3 wt/wt % propanediol; and greater than or equal to 0.25 wt/wt % and less than or equal to 2 wt/wt % dimethyl isosorbide. In embodiments, a wt/wt % ratio of propanediol to dimethyl isosorbide may be from 1.00:1 to 3.00:1.

Abstract: Systems and methods are proposed for verifying whether a digital twin of abiological asset (or biological system) of a subject (110) indeed relates to the subject (110). Such proposals may be based on obtaining: a first signal comprising a value of a physiological parameter of the subject (e.g. determined by a physical and/or virtual sensor arrangement); and a second signal generated by the digital twin that simulates a value of a physiological parameter of the subject (110). By comparing the first and second signals, a verification result may then be determined.

Abstract: The invention implements a digital twin to provide e.g. estimated measurements of one or more physiological or anatomical parameters in a way that requires less local storage space and local processing capacity. Embodiments are based on constructing and implementing a lean digital twin model or dataset by storing results of a number of full digital model simulations in advance and then using these pre-stored simulation results (42) to provide physiological parameter estimations, in place of the full digital twin model (32).

Abstract: A provided method is for configuring settings of a computing device for providing more efficient and reliable acquisition of data for use in updating a personalized digital model (digital twin) of a subject. The method comprises configuring settings of a biometric authentication function of a computing device so as to provide for overlap in the input data requirements of the biometric authentication function and the input data requirements of the digital twin. There may be different authentication protocols available at the computing device, each requiring different input sensing data. Based on knowledge of these different authentication protocols and data requirements, and based on knowledge of data input needs of a digital twin, an authentication protocol can be selected and/or its settings adjusted, so that when performing biometric authentication, the same acquired sensor data can also be used for deriving physiological parameter data of the subject, for updating the digital twin.

Abstract: 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.

Abstract: Systems and methods are proposed for training and employing a machine learning algorithm for identifying uncertain Digital Twin model parameters. Such proposals may make use of a set of DTs that is chosen such that it comprises DTs that have achieved a target/predetermined morbidity scenario. The training may then identify uncertain model parameters for the set of DTs. Using the identified uncertain model parameters, the set of DTs may then be run so as to assess whether the target morbidity scenario is achieved.

Abstract: A system (10) configures operating settings and parameters of a patient monitor (14), in particular a bedside patient monitor, based on use of a digital twin (32) of the at least one patient (13) being monitored. The system (10) configures settings of a signal processing module (16) of the patient monitor, the signal processing module adapted to process incoming sensor data to extract or derive one or more clinical parameters relating to the patient. Outputs of the digital twin relate to a current or predicted future state of the patient and thus can intelligently guide the signal processing module to extract and output clinical parameters which are most clinically relevant to the patient's condition at any given time. Thus the patient monitor (14) automatically displays the most clinically relevant parameters for the patient at each given moment, which allows a clinician to rapidly ascertain the clinical state of the patient, and to take any necessary action quickly to treat the patient.

Abstract: A system (10) for configuring data collection for provision to a digital twin for generating a desired set of output information (e.g. physiological or anatomical parameter estimations) from the digital twin. The system is configured to detect available sources (56) of physical sensor data (58) pertaining to a patient, and to compare these with determined input data requirements of a digital twin (32) of the patient for computing a particular defined set of required output information. Depending on the result, a virtual sensing module (28) can be used to generate supplementary ‘virtual’ sensor data to compensate for any insufficiencies in the available physical sensor data or augment the physical sensor data, and the system can re-configure operating settings of the available physical sensors (56) to optimally meet the input data requirements of the digital model.

Abstract: A system (10) for configuring operation settings of a medical device (16) in a state of interaction with a patient (14), and determining features or options of a patient treatment, is based on use of co-operative feedback between digital models (34, 32) for the medical device (16) on the one hand and at least a portion of the anatomy with which the medical device is interaction on the other. A feedback loop is implemented between outputs (46, 48) and inputs (42, 44) of the two models, so that outputs of each model form at least part of the input provided to the other of the models. This way a decision outcome regarding optimum operation settings for the medical device, for balancing both medical aims and also operational efficiency aims for the medical device, can effectively be arrived at iteratively through recursive circular feedback between the two models. The decision outcome may be refined though this process of back and forth feedback.

Abstract: Systems and methods are proposed for controlling a Digital Twin of a biological asset of a subject based on: (i) characteristics of a user of the DT; (ii) a status of the subject; and (iii) previous usage of the DT. Such control is facilitated through the generation of one or more parameter values for the DT. For instance, the parameter value(s) may define one or more elements or components of the DT, such as program code, input requirements, output characteristics or a value of a tunable element of the DT.

Abstract: Disclosed is a method (100) of providing a personalized parameter model of at least part of the physiology of a person (10) carrying a set of sensors (12, 14, 16) with a computer system (20, 50) comprising a processor arrangement (22, 52) for the purpose of updating a biophysical model or digital twin of the person. The method comprises, with said processor arrangement, receiving (103) 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 (105) the received sensor data from each sensor to define the personalized parameter model (3), 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 (107) the defined personalized parameter model for updating a digital model (1) of at least part of the anatomy of said person.

Abstract: An approach is proposed for managing human operators (22), the approach comprising managing power delivery to sensor components (24) carried by the human operators based on a need for the human operators for performing a task. The human operators (e.g. clinical staff in a hospital) carry sensors for monitoring location and physiological parameters of the staff. An operation mode of the sensors (24) is altered (e.g. the sensors (24) are activated) based on resource request signals received indicative of need for a particular kind of human assistance. Out of the human operators (22) capable of providing the needed kind of human assistance, one or more human operators are selected based on received sensor data from the sensor sets (24) relating to the location and the physiological parameters.

Abstract: Systems and methods are proposed for assessing digital twin accuracy by processing first and second input data to generated target output data and supplementary output data, respectively. Based on the target output data and the supplementary output data, a value of accuracy of the DT may be determined.

Abstract: Hair shine compositions are disclosed. In embodiments, a hair shine composition may include greater than or equal to 50 wt/wt % and less than or equal to 95 wt/wt % water; greater than or equal to 0.5 wt/wt % and less than or equal to 3 wt/wt % propanediol; and greater than or equal to 0.25 wt/wt % and less than or equal to 2 wt/wt % dimethyl isosorbide. In embodiments, a wt/wt % ratio of propanediol to dimethyl isosorbide may be from 1.00:1 to 3.00:1.

Abstract: It is proposed to combine a multi-organ in-vitro system with a multi-organ in-silico system to provide improved modelling/prediction of multi-organ interaction(s) for a subject. By way of example, a subject-specific multi-organ in-vitro system may undergo perturbation. The response of the other organ(s) to the perturbation modelled by the in-vitro model may then be identified, and this response may then be used in the in-silico model to predict the outcome(s) of the perturbation(s).