Abstract: A system is provided for determining the position of an interventional device inside a lumen. The interventional device comprises a distal portion inside the lumen and a proximal portion outside the lumen and the system comprises a processor. The processor is configured to receive a first estimated position of the distal portion of the interventional device inside the lumen from a first system and receive an estimated displacement of the distal portion of the interventional device inside a lumen from a second system, wherein the estimated displacement is representative of the movement of the distal portion of the interventional device inside the lumen. The processor is further configured to determine a second estimated position of the distal portion of the interventional device inside the lumen based on the first estimated position and the estimated displacement.

Abstract: Disclosed is an in vitro method for assessing whether a human subject has periodontitis. The method comprises detecting, in a sample of saliva from said subject, the concentrations of the proteins Hepatocyte Growth Factor (HGF) and Matrix Metalloproteinase 8 (MMP-8). Based on the concentrations determined, and adding age, and possibly other demographic markers such as sex and/or BMI, a testing value reflecting the joint concentrations is determined for said proteins, in combination with one or more demographic markers. The testing value is compared with a threshold value. The threshold reflects in the same manner the joint concentrations and the age, and possibly other demographic markers, as associated with periodontitis and may be seen as an upper limit of testing values as seen in a population of subjects without periodontitis. Thereby a testing value at or above the threshold value is indicative for periodontitis in said subject.

Abstract: Disclosed is an in vitro method for assessing whether a human subject has periodontitis. The method comprises detecting, in a sample of saliva from said subject, the concentrations of the proteins Hepatocyte Growth Factor (HGF) and Matrix Metalloproteinase 8 (MMP-8). Based on the concentrations determined, and adding age, and possibly other demographic markers such as sex and/or BMI, a testing value reflecting the joint concentrations is determined for said proteins, in combination with one or more demographic markers. The testing value is compared with a threshold value. The threshold reflects in the same manner the joint concentrations and the age, and possibly other demographic markers, as associated with periodontitis and may be seen as an upper limit of testing values as seen in a population of subjects without periodontitis. Thereby a testing value at or above the threshold value is indicative for periodontitis in said subject.

Abstract: The invention relates a computer-implemented method (500) of generating explainability information for explaining a model output of a trained model. The method uses one or more aspect recognition models configured to indicate a presence of respective characteristics in the input instance. A saliency method is applied to obtain a masked source representation of the input instance at a source layer of the trained model (e.g., the input layer or an internal layer), comprising those elements at the source layer relevant to the model output. The masked source representation is mapped to a target layer (e.g., input or internal layer) of an aspect recognition model, and the aspect recognition model is then applied to obtain a model output indicating a presence of the given characteristic relevant to the model output of the trained model. As explainability information, the characteristics indicated by the aspect recognition models are output.

Abstract: The present invention relates to clinical decision support systems. In detail, the present invention relates to a method for determining the initial fibrinogen concentration in a biological sample, to the use of fibrinogen added to a biological sample in at least one predetermined concentration, to a device for determining the initial fibrinogen concentration in a biological sample, to a computer program comprising program code means for causing a computer to carry out at least several steps of the method according to the invention, to a computer readable non-transitory storage medium containing instructions for carrying out at least some steps of the method according to the invention, and to a kit for determining the initial fibrinogen concentration in a biological sample.

Abstract: Aspects and embodiments relate to a method of providing a representation of a feature identified by a deep neural network as being relevant to an outcome, a computer program product and apparatus configured to perform that method.

Abstract: The present invention provides a method, computer program and processing system for identifies boundaries of lesions within image data. The image data is processed using a machine learning algorithm to generate probability data and uncertainty data. The probability data provides, for each image data point of the image data, a probability data points indicating a probability that said image data point is part of a lesion. The uncertainty data provides, for each probability data point, an uncertainty data point indicating an uncertainty of the said probability data point. The uncertainty data is processed to identify or correct boundaries of the lesions.

Abstract: Presented are concepts for feature identification in medical imaging of a subject. One such concept processes a medical image with a Bayesian deep learning network to determine a first image feature of interest and an associated uncertainty value, the first image feature being located in a first sub-region of the image. It also processes the medical image with a generative adversarial network to determine a second image feature of interest within the first sub-region of the image and an associated uncertainty value. Based on the first and second image features and their associated uncertainty values, the first sub-region of the image is classified.

Abstract: The present invention relates to device for determining a fibrinogen level in a sample comprising, a first input for obtaining an attenuance signal over time indicative of a fibrin polymerization of said sample, a second input for obtaining a reactant concentration signal over time indicative of a reactant concentration in said sample, wherein the reactant is any substance leading to the cleavage of fibrinogen to fibrin, a simulation unit running a model using the reactant concentration signal as an input to provide a simulated attenuance signal over time, and an evaluation unit configured to infer the fibrinogen level of said sample by comparing the attenuance signal over time with the simulated attenuance signal over time.

Abstract: The present invention relates to device (10) for determining a fibrinogen level (20) in a sample (22) comprising, a first input for obtaining an attenuance signal (24) over time indicative of a fibrin polymerization of said sample (22), a second input for obtaining a reactant concentration signal (28) over time indicative of a reactant concentration in said sample (22), wherein the reactant is any substance leading to the cleavage of fibrinogen to fibrin, a simulation unit (16) running a model (32) using the reactant concentration signal (28) as an input to provide a simulated attenuance signal (34) over time, and an evaluation unit (18) configured to infer the fibrinogen level (20) of said sample (22) by comparing the attenuance signal (24) over time with the simulated attenuance signal (34) over time.

Abstract: The invention relates to a trained model, such as a trained neural network, which is trained on training data. System and computer-implemented methods are provided for generating metadata which encodes a numerical characteristic of the training data of the trained model, and for using the metadata to determine conformance of input data of the trained model to the numerical characteristics of the training data. If the input data does not conform to the numerical characteristics, the use of the trained model on the input data may be considered out-of-specification (‘out-of-spec’). Accordingly, a system applying the trained model to the input data may, for example, warn a user of the non-conformance, or may decline to apply the trained model to the input data, etc.

Abstract: Presented are concepts for feature identification in medical imaging of a subject. One such concept processes a medical image with a Bayesian deep learning network to determine a first image feature of interest and an associated uncertainty value, the first image feature being located in a first sub-region of the image. It also processes the medical image with a generative adversarial network to determine a second image feature of interest within the first sub-region of the image and an associated uncertainty value. Based on the first and second image features and their associated uncertainty values, the first sub-region of the image is classified.

Abstract: Disclosed is an in vitro method for assessing whether a human subject has periodontitis. The method comprises detecting, in a sample of saliva from said subject, the concentrations of the proteins Hepatocyte Growth Factor (HGF) and Matrix Metalloproteinase 8 (MMP-8). Based on the concentrations determined, and adding age, and possibly other demographic markers such as sex and/or BMI, a testing value reflecting the joint concentrations is determined for said proteins, in combination with one or more demographic markers. The testing value is compared with a threshold value. The threshold reflects in the same manner the joint concentrations and the age, and possibly other demographic markers, as associated with periodontitis and may be seen as an upper limit of testing values as seen in a population of subjects without periodontitis. Thereby a testing value at or above the threshold value is indicative for periodontitis in said subject.

Abstract: The invention relates to a method for predicting a value of a parameter of a system (20). The value of the parameter of the system (20) is predicted by incrementally optimizing a pharmacokinetic and pharmacodynamic model based on values of parameters of the system (20) received over time. This allows for predicting values of the parameters of the system (20) with improved accuracy. In one embodiment the system (20) comprises a coagulation system (21) comprising an anticoagulant. The predicted value of the parameter of the system (20) can be a point in time at which the coagulation system (21) reaches hemostatic balance after a periodic supply of anticoagulant to the coagulation system (21) is discontinued.

Abstract: The present invention relates to a method for estimating a value of a parameter of a blood coagulation system in a subject that is periodically supplied with an anticoagulant, wherein the parameter of the coagulation system is a time taken for the coagulation system to reach a state of coagulation after periodic supply of anticoagulant to the subject is discontinued. The value of the parameter of the coagulation system is estimated based on concentration levels of 5 or 6 coagulation proteins derived from the coagulation system at a predetermined point in time relative to a point in time of supply of a last dose of anticoagulant to the coagulation system. In one embodiment the 5 coagulation proteins are coagulation factor VII (F7), coagulation factor IX (F9), Anti-thrombin (AT), coagulation factor X (F10), and coagulation factor VIII (F8). Optionally the 6th coagulation protein is coagulation factor XI (F11).

Abstract: A method and system for the assessment of the risk of development of disseminated intravascular coagulation (DIC), in patients showing systemic inflammatory response syndrome (SIRS) or sepsis is disclosed. Specifically, the invention provides a method for early DIC assessment and preventive treatment planning, which has the potential for significantly decreasing mortality rate as well as the rate of DIC related sequelae in the SIRS/sepsis patient population and thereby improving quality of life. The risk assessment method is based on features of vital signs and/or biomarker measurements, and provides solutions for assessing the risk of DIC development 24 hours in advance or within 72 hours after ICU admittance.

Abstract: The present invention relates to clinical decision support systems. In detail, the present invention relates to a method for determining the initial fibrinogen concentration in a biological sample, to the use of fibrinogen added to a biological sample in at least one predetermined concentration, to a device for determining the initial fibrinogen concentration in a biological sample, to a computer program comprising program code means for causing a computer to carry out at least several steps of the method according to the invention, to a computer readable non-transitory storage medium containing instructions for carrying out at least some steps of the method according to the invention, and to a kit for determining the initial fibrinogen concentration in a biological sample.

Abstract: The present invention relates to device (10) for determining a fibrinogen level (20) in a sample (22) comprising, a first input for obtaining an attenuance signal (24) over time indicative of a fibrin polymerization of said sample (22), a second input for obtaining a reactant concentration signal (28) over time indicative of a reactant concentration in said sample (22), wherein the reactant is any substance leading to the cleavage of fibrinogen to fibrin, a simulation unit (16) running a model (32) using the reactant concentration signal (28) as an input to provide a simulated attenuance signal (34) over time, and an evaluation unit (18) configured to infer the fibrinogen level (20) of said sample (22) by comparing the attenuance signal (24) over time with the simulated attenuance signal (34) over time.

Abstract: The present invention relates to clinical decision support systems. In detail, the present invention relates to a method for determining the hemostatic risk of a subject, to the use of a biomarker's threshold for determining the hemostatic risk of a subject, to a device for determining the hemostatic risk of a subject, to a computer program comprising a program code for carrying out the method for determining the hemostatic risk of a subject, and to a computer-readable non-transitory storage medium containing instructions for carrying out the method for determining the hemostatic risk of a subject.

Abstract: The present invention relates to an apparatus and method for clinical decision support to identify patients at high risk of thrombosis based on a combination of clinical risk factors and molecular markers, e.g., protein concentrations. These clinical risk factors and molecular markers are combined in a machine learning based algorithm which returns an output value, relating to an estimated risk of a thrombosis event in the future.