Abstract: The invention relates to a system, an ultrasound probe and a corresponding method for measuring arterial parameters using non-imaging ultrasound. The system comprises an acquisition unit for acquiring doppler ultrasound signal from a blood vessel and a processing unit for processing the acquired doppler ultrasound signal and to determine the changes in the blood vessel through the measurements of at least Peak Systolic Velocity (PSV) and Pulse Wave Velocity (PWV). The acquisition unit comprises an ultrasound probe having a plurality of transducer elements arranged in a grid configuration, and comprising a first probe (102a) and a second probe (102b) detachably connected to each other. In the split configuration the ultrasound probe is provided to measure the PWV globally between the carotid and femoral arteries, or the PSV and PWV locally and simultaneously. In the integrated configuration the PSV or PWV may be measured locally.

Abstract: A non-transitory computer readable medium (107, 127) stores instructions executable by at least one electronic processor (101, 113) to perform a component co-replacement recommendation method (200).

Abstract: According to an aspect, there is provided a method of training a model to perform a task on medical data using a distributed machine learning process whereby a global model is updated based on training performed on local copies of the model at a plurality of clinical sites. The method comprises a) sending (302) information to the plurality of clinical sites to enable each of the plurality of clinical sites to create a local copy of the model and train the respective local copy of the model on training data at the respective clinical site. The method then comprises b) receiving (304), from each of the plurality of clinical sites, i) a local update to a parameter in the model obtained by training the local copy of the model on the training data at the respective clinical site and ii) metadata related to a quality of the training performed at the respective clinical site; and c) updating (306) the parameter in the global model, based on the received local updates to the parameter and the received metadata.

Abstract: According to an aspect there is provided a computer-implemented method of monitoring performance of a predictive computer-implemented model, PCIM, that is used to monitor the status of a first system. The PCIM receives as inputs observed values for a plurality of features relating to the first system, and the PCIM determines whether to issue status alerts based on the observed values.

Abstract: A device, system, and method is provided for detecting pain in a cardiac-related region of the body and determining whether that pain is cardiac or non-cardiac. The device, system, and method may include calculating or determining a first feature based on a variation in activity level and a variation in the detected heartrate measurement and a second feature based on a variation in the detected ECG features and a first feature and then subjecting at least the first feature and the second feature to a cardiac pain classifier to determine a cardiac classification.

Abstract: Embodiments of present disclosure disclose method and system for generating a medical image based on a textual data in a medical report. For generation, a textual data from each of one or more medical reports of the patient is retrieved. The textual data comprises one or more medical events and corresponding one or more attributes associated with each of the one or more medical reports. Further, a matching score for each of plurality of reference images is computed based on the textual data, using a first machine learning model. Upon computing the matching score, one or more images are selected from the plurality of reference images based on the matching score associated with each of the plurality of reference images. The medical image for the patient is generated based on the one or more images and the textual data using a second machine learning model.

Abstract: A non-transitory computer readable medium (107, 127) stores instructions executable by at least one electronic processor (101, 113) to perform a component co-replacement recommendation method (200).

Abstract: A device for optimizing an image acquisition device (12) includes at least one electronic processor (20) operatively connected to read a machine log (30) of the image acquisition device. A non-transitory computer readable medium (26) stores instructions readable and executable by the at least one electronic processor to perform an image acquisition method (100). The method includes: extracting logged parameters of the image acquisition device from the machine log of the image acquisition device; identifying one or more out-of-range parameters of the image acquisition device from the logged parameters extracted from the machine log of the image acquisition device; automatically tuning one or more electrical or mechanical settings of the image acquisition device on the basis of the one or more out-of-range parameters to transform the image acquisition device into a tuned image acquisition device; and controlling the tuned image acquisition device to acquire one or more images of a patient.

Abstract: A central signal segregation station (100) employs a signal acquisition controller (103) and a signal segregation controller (104). In operation, the signal acquisition controller (103) receives a plurality of different types of physiological signals from a plurality of unknown physiological sensors (10; 20; 30; 40; 50; 60; 70; 80). For a monitoring of the physiological signals, the signal segregation controller (104) identifies a particular type of each physiological signal based on distinct signal features of each physiological signal corresponding to a different physiological signal model (101) among a plurality of physiological signal models (101) derived from known types of physiological sensors.

Abstract: The invention provides for a medical imaging system (100, 300, 400, 700) comprising: a memory (110) for storing machine executable instructions (120) and a processor (106) for controlling the medical imaging system.

Abstract: In a monitoring method for generating maintenance alerts, component IDs are extracted which identify medical imaging device components in electronic medical imaging device manuals (25, 26, 27, 28). Operating parameters of the medical imaging device components and associated operating parameter ranges are also extracted from the manuals, based on numeric values, parameter terms identifying operating parameters, and linking terms or symbols indicative of equality or inequality that connect the numeric values and parameter terms. The operating parameter ranges are formulated into decision rules (36) which are applied to log data (40) generated by a monitored medical imaging device (2) to detect out-of-range log data generated by the monitored medical imaging device. Maintenance alerts (24) are displayed on a display (18) in response to the detected out-of-range log data. The maintenance alerts are generated from out-of-range log data and are associated with component IDs contained in the out-of-range log data.

Abstract: Embodiments of present disclosure disclose method and system for generating a medical image based on a textual data in a medical report. For generation, a textual data from each of one or more medical reports of the patient is retrieved. The textual data comprises one or more medical events and corresponding one or more attributes associated with each of the one or more medical reports. Further, a matching score for each of plurality of reference images is computed based on the textual data, using a first machine learning model. Upon computing the matching score, one or more images are selected from the plurality of reference images based on the matching score associated with each of the plurality of reference images. The medical image for the patient is generated based on the one or more images and the textual data using a second machine learning model.

Abstract: A method (200) of converting a flowchart (110) to a structured electronic representation (116) of the flowchart includes: identifying, in an image (108) of the flowchart, a plurality of shapes (112) corresponding to flowchart blocks of the flowchart; identifying arrows (114) defining flow paths between the flowchart blocks in the image including identifying flowchart blocks connected by the arrows and their directionality; identifying text labels and their locations in the image and determining text content of the text labels; associating the text labels with flowchart blocks or defined flow paths based on locations of the text labels, flowchart blocks, and arrows; and generating the structured electronic representation of the flowchart based on the flowchart blocks, the flow paths, and the text labels. A structure of the structured electronic representation is determined at least based at least on the flow paths between the flowchart blocks of the image.

Abstract: A device, system, and method is provided for detecting pain in a cardiac-related region of the body and determining whether that pain is cardiac or non-cardiac. The device, system, and method may include calculating or determining a first feature based on a variation in activity level and a variation in the detected heartrate measurement and a second feature based on a variation in the detected ECG features and a first feature and then subjecting at least the first feature and the second feature to a cardiac pain classifier to determine a cardiac classification.

Abstract: The present invention relates to a method of automatic labeling of activity of a subject on ECG data. The method of the invention comprises acquiring at least one physiological input signal purporting to an ECG signal and processing thereof. The processing of the at least one physiological input signal comprises conditioning the ECG signal and processing thereof, wherein the processing comprises obtaining respiration data from the ECG signal, identifying the activity pertaining to the said ECG data based on at least a signal specific feature of the said ECG signal, wherein the respiration data are used for differentiating activities performed by the subject, and labeling the said ECG data with the said activity, automatically. The present invention also relates to a system for automatic labeling of activity on ECG data in accordance with the method of the invention.

Abstract: A central signal segregation station (100) employs a signal acquisition controller (103) and a signal segregation controller (104). In operation, the signal acquisition controller (103) receives a plurality of different types of physiological signals from a plurality of unknown physiological sensors (10; 20; 30; 40; 50; 60; 70; 80). For a monitoring of the physiological signals, the signal segregation controller (104) identifies a particular type of each physiological signal based on distinct signal features of each physiological signal corresponding to a different physiological signal model (101) among a plurality of physiological signal models (101) derived from known types of physiological sensors.

Abstract: The invention relates to a system, an ultrasound probe and a corresponding method for measuring arterial parameters using non-imaging ultrasound. The system comprises an acquisition unit for acquiring doppler ultrasound signal from a blood vessel and a processing unit for processing the acquired doppler ultrasound signal and to determine the changes in the blood vessel through the N measurements of at least Peak Systolic Velocity (PSV) and Pulse Wave Velocity (PWV). The acquisition unit comprises an ultrasound probe having a plurality of transducer elements arranged in a grid configuration, and comprising a first probe (102a) and a second probe (102b) detachably connected to each other. In the split configuration the ultrasound probe is provided to measure the PWV globally between the carotid and femoral arteries, or the PSV and PWV locally and simultaneously. In the integrated configuration the PSV or PWV may be measured locally.

Abstract: The present invention relates to a method of automatic labeling of activity of a subject on ECG data. The method of the invention comprises acquiring at least one physiological input signal purporting to an ECG signal and processing thereof. The processing of the at least one physiological input signal comprises conditioning the ECG signal and processing thereof, wherein the processing comprises obtaining respiration data from the ECG signal,identifying the activity pertaining to the said ECG data based on at least a signal specific feature of the said ECG signal, wherein the respiration data are used for differentiating activities performed by the subject,and labeling the said ECG data with the said activity, automatically. The present invention also relates to a system for automatic labeling of activity on ECG data in accordance with the method of the invention.

Abstract: The present invention relates to a system for measuring the arterial parameters. The system of the invention comprises a signal unit for providing radio frequency (RF) ultrasound signal and demodulated RF ultrasound signal and of the data relating thereto from the signal acquired therefrom; a detection unit for detecting the presence of blood flow in an artery; an identification unit for identifying the said artery; a processing unit for processing the said data and for providing the distension waveform of the said artery; and an estimating unit for estimating at least one of a plurality of localized arterial parameters of the said artery. The invention also relate to a method for measuring the arterial parameters by the system of the invention. The invention has the advantage of measuring localized blood pressure continuously and also other localized arterial parameters such as Peak Systolic Velocity, Pulse Wave Value and arterial compliance measures, in a non-imaging, non-invasive and cuff-less manner.

Abstract: A system fits a curve to a filtered ECG signal, processes the fitted curve (e.g., by applying transforms such as a KL Transform) and derives parameters for use in classifying heart cycle signal portions (such as an ST segment portion) into particular heart cycle signal portion categories associated with particular segment morphology. A system for heart signal classification includes an interface for receiving an electrical signal waveform comprising an R-wave and including an ST segment portion associated with heart electrical activity of a patient over a heart beat cycle. A signal processor processes data representing the electrical signal waveform by fitting a curve to the ST segment and applying a transform to the fitted curve to derive variance data indicating variance in the fitted curve. A signal classifier classifies the ST segment into one of multiple predetermined categories associated with the fitted ST segment curve geometry in response to the derived variance data.