CLINICAL SUPPORT SYSTEM AND ASSOCIATED COMPUTER-IMPLEMENTED METHODS

    公开(公告)号:US20240135736A1

    公开(公告)日:2024-04-25

    申请号:US18485600

    申请日:2023-10-12

    Abstract: A clinical support system comprises a processor and a display component, wherein: the processor is configured to: receive image data, the image data representing an image of a plurality of cells obtained from a human or animal subject, the image data comprising a plurality of subsets of image data, each subset comprising data representing a portion of the image data corresponding to a respective cell of the plurality of cells; apply a trained deep learning neural network model to each subset of the image data, the deep learning neural network model comprising: a plurality of convolutional neural network layers each comprising a plurality of nodes; and a bottleneck layer comprising no more than ten nodes, wherein the processor is configured to apply the trained deep learning neural network model to each subset of the image data by applying the plurality of CNN layers, and subsequently applying the bottleneck layer, each node of the bottleneck layer of the machine-learning model configured to output a respective activation value for that subset of the image data; for each subset of the image data, derive a dataset comprising no more than three values, the values derived from the activation values of the nodes in the bottleneck layer; and generate instructions, which when executed by the display component of a clinical support system, cause the display component of the computer to display a plot in no more than three dimensions of the respective dataset of each subset of the image data. Associated computer-implemented methods, including for training the deep learning neural network model, are provided.

    Liquid chromatography—stream equivalence by single stream calibration

    公开(公告)号:US11940427B2

    公开(公告)日:2024-03-26

    申请号:US17448063

    申请日:2021-09-20

    CPC classification number: G01N30/7266 G01N30/88 G01N2030/027

    Abstract: A liquid chromatography-mass spectrometry (LC-MS) apparatus including an ionization source coupled to a mass spectrometer and a liquid chromatographic (LC) system coupled to the ionization source. The LC system comprises multiple fluidic streams alternately connectable to the ionization source, thereby assigning a detection time window to each fluidic stream from the multiple fluidic streams when connected to the ionization source. The LC-MS apparatus further comprises a controller configured to carry out steps of monitoring an ionization current of the ionization source for the multiple fluidic streams and identifying differences in flow conditions between the multiple fluidic streams based on the monitored ionization current. The controller is further configured to carry out adjusting detection conditions of one or more of the multiple fluidic streams responsive to the identified differences, thereby enabling eluates of interest from each fluidic stream to be detected by the mass spectrometer in the respective detection time window.

    MEDICAL LABORATORY COMPUTER SYSTEM
    356.
    发明公开

    公开(公告)号:US20240096456A1

    公开(公告)日:2024-03-21

    申请号:US18463834

    申请日:2023-09-08

    CPC classification number: G16H10/40 G06F21/602 G16H10/60 H04L63/0421

    Abstract: A healthcare computer system, the computer system comprising a communications module, a string generation module, a one-way function module and an anonymised data generation module. The communications module is configured to receive one or more healthcare data packets, each healthcare data packet including: data pertaining to one or more medical analytical tests performed on a sample; a sample identifier, identifying the sample; and a timestamp, indicating when the analytical test(s) was performed. The string generation module is configured to generate a string based on the sample identifier and the timestamp. The one-way function module is configured to apply a one-way function to the generated string to generate an anonymised sample identifier. The anonymised data generation module is configured to generate an anonymised healthcare data packet including the data pertaining to the one or more medical analytical tests and the anonymised sample identifier.

    HEALTHCARE DATA PROCESSING CAPACITY MANAGEMENT
    357.
    发明公开

    公开(公告)号:US20240079101A1

    公开(公告)日:2024-03-07

    申请号:US18457388

    申请日:2023-08-29

    CPC classification number: G16H10/40 G06F9/4843 G16H40/20

    Abstract: A healthcare data management system for managing processing capacity in a healthcare data management system. The healthcare data management system includes: one or more processing pipelines connected to one or more of the medical devices and configured to receive medical data therefrom, wherein each processing pipeline comprises a plurality of processing stages arranged in series and configured to perform respective operations on the received healthcare data, wherein each processing stage is implemented on a stateless atomic processing unit; a healthcare middleware is configured to receive processed data therefrom and to provide the processed data to a healthcare information management system; a performance management unit is configured to monitor a performance of the or each processing pipeline and adjust a number of stateless atomic processing units implementing a given processing stage within a given processing pipeline based on the monitored performance.

    Bucket insert for use in a centrifuge

    公开(公告)号:US11890623B2

    公开(公告)日:2024-02-06

    申请号:US16953868

    申请日:2020-11-20

    CPC classification number: B04B7/12 B04B5/0421

    Abstract: A bucket insert for use in a centrifuge is disclosed. The bucket insert comprises an insert body, the insert body comprising a plurality of elongated receptacles for receiving elongated sample vessels. The bucket insert is configured for orienting the elongated sample vessels in a tilted orientation. In the tilted orientation, at least some of the elongated sample vessels are oriented in a non-parallel fashion.

    LABORATORY DATA MANAGEMENT SYSTEM
    359.
    发明公开

    公开(公告)号:US20230368874A1

    公开(公告)日:2023-11-16

    申请号:US18360355

    申请日:2023-07-27

    CPC classification number: G16H10/40 G16H40/40

    Abstract: The present disclosure relates to a laboratory data management system 100 comprising a data source layer 10 comprising at least one laboratory device 11, 12, 13, 14, 15 as a data source, a data adapter layer 20 comprising at least one data source agent 21, 22, 23, 24, 25 configured to obtain data from the at least one data source and to convert the data from a data source format to a data-source independent format, a consumer layer 30 configured for installation and/or execution of consumer application software 31, 32, 33, 34, and a data management layer 40 between the data adapter layer 20 and the consumer layer 30. The data management layer 40 comprises a data storage 50 configured to store data 1-n converted by the at least one data source agent 21, 22, 23, 24, 25, at least one data manager 41, 42, 43, 44 programmed to execute instructions from the consumer application software 31, 32, 33, 34, which when executed cause the at least one data manager 41, 42, 43, 44 to select application-specific data in the data storage 50 and to make them accessible to a consumer via the consumer application software 31, 32, 33, 34 in a consumer format. The data adapter layer 20 and the data management layer 40 are arranged in a laboratory gateway 60 connected to the data-source layer 10 and to the consumer layer 30.

    Maintenance method for a laboratory system

    公开(公告)号:US11817212B2

    公开(公告)日:2023-11-14

    申请号:US16907605

    申请日:2020-06-22

    CPC classification number: G16H40/67 G16H40/40 G06F11/004 G06N7/01 G16H10/40

    Abstract: A maintenance method for a laboratory system comprising a first and second group of laboratory instruments for processing biological samples, data collection components connected to the groups of instruments, and a remote maintenance system connected to the data collection components is presented. The method comprises collecting operational data from the laboratory instruments by the data collection components, detecting an anomaly related to the laboratory instruments by a first data collection component, transmitting context data to the remote maintenance system upon detection of an anomaly, determining correlation(s) between the operational data and the anomaly(s), validating the correlation(s), determining at the remote maintenance system predictive rules corresponding to validated correlations, transmitting the predictive rule(s) to the data collection components, and predicting occurrence of an anomaly of laboratory instruments based on the one or more predictive rule(s) by the data collection components.

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