57 results about "Diagnostic laboratory" patented technology

The invention relates to an intensive care cross-institution collaborative data processing method, which comprises the following steps of: defining a hierarchical data model based on the intensive care field, and modeling various types of information including vital signs, clinical diagnosis and laboratory detection results of a patient according to a time sequence; a self-adaptive standardized interface is designed, and data formats from different hospitals or devices are identified and converted; real-time dynamic tags are introduced for patient data through a tagging technology; various health data, including vital signs, medical history and treatment feedback, of a critical patient are converted into a multi-dimensional health track, and sequential analysis is used for conducting continuous modeling on the condition of the patient; a deep learning and reinforcement learning method is combined to establish a disease evolution deduction engine; the disease development is predicted according to historical data, and the influence of different intervention measures on the disease is simulated to intelligently recommend a treatment strategy; and according to the dynamic deduction result, generating a plurality of treatment schemes, and dynamically adjusting the intervention strategy according to the real-time response of the patient.

The invention discloses an endometrial cancer diagnosis method based on a multi-mode flexible classification network, and the method comprises the steps: firstly, employing a knowledge distillation strategy, processing MRI images including a high-quality mode and an inferior mode through a student sub-network and a teacher sub-network, and enhancing the applicability and robustness of a model; secondly, a cross-modal feature enhancement module and a hierarchical fusion strategy are utilized to fully explore heterogeneity features and complementarity information among different modals, and the extraction capability and classification performance of a network model on key pathological features are enhanced; then, a flexible classification network structure is adopted to carry out self-adaptive weight adjustment and enhanced fusion on the multi-scale features of the multi-modal MRI image, and the diagnosis advantages of the multi-modal MRI image on endometrial cancer are exerted to the maximum extent; on the basis, a deep neural network is utilized to classify and diagnose canceration areas of the endometrium. Experimental results verify the effectiveness of the method.

A method of synthesizing an image of a tube assembly includes capturing an image of the tube assembly, wherein the capturing generates a captured image. The captured image is decomposed into a plurality of features in latent space using a trained image decomposition model. One or more of the features in the latent space is manipulated into one or more manipulated features. A synthesized tube assembly image is generated with at least one of the manipulated features using a trained image composition model. Other methods and systems are disclosed.

A method of analyzing inventory in a diagnostic laboratory system includes capturing images of a plurality of inventory items used in the diagnostic laboratory system, wherein the capturing generates image data; analyzing the image data to identify one or more inventory items; and generating, in response to the analysis, an instruction to cause a printer to print one or more identification tags, wherein each of the one or more identification tags includes identification data identifying one of the one or more inventory items. Other methods and systems are disclosed.

A system and method are provided for predicting a clinical diagnostic laboratory's readiness to meet regulatory proficiency testing (PT) standards, such as the CLIA 2024 regulatory standards. The system obtains laboratory performance data including Lab Mean, Lab Standard Deviation (SD), and Peer Mean for a set of analytes, and models expected result distributions. Using regulatory acceptance limits, the system computes the probability of individual result failure, PT event failure, and regulatory sanction based on binomial models. The system may generate graphical outputs such as distribution overlays and risk contour plots, enabling laboratories to proactively evaluate and improve PT performance. The system may be deployed as a standalone tool or integrated into a laboratory quality management environment.

A diagnostic laboratory distribution system, wherein the distribution system comprises a number of carriers adapted to carry one or more goods. The distribution system comprises a transport plane adapted to support the carriers and a drive device, wherein the drive device is adapted to move the carriers on the transport plane, and a control device to control the drive device. The distribution system comprises a cover for the transport plane, and humidity sensors connected to the control device and airflow generating devices connected to the control device. The airflow generating devices are distributed over the distribution system to generate an airflow between the cover and the transport plane. The control device is configured to start the airflow generating devices if the humidity sensors measure a humidity above a predefined threshold value.

In some embodiments, a method of path finding for sample containers in diagnostic laboratory systems includes receiving a batch of sample containers in a diagnostic laboratory system having diagnostic laboratory equipment; obtaining a grid for the diagnostic laboratory system, the grid having cells that include the diagnostic laboratory equipment and one or more tracks connecting the diagnostic laboratory equipment; assigning an agent to each of the sample carriers; determining, for each agent assigned to a sample carrier holding a sample container, a goal including an earliest arrival time constraint, a deadline constraint, and a goal processing time constraint; and employing a multi-agent path finding (MAPF) trained neural network to determine an action for each agent, the neural network trained with arrival, deadline, and precedence constraints and with non-instantaneous goal processing times. Numerous other aspects are provided.

In some embodiments, a method for path finding of sample containers in a diagnostic laboratory system includes receiving a batch of sample containers in a diagnostic laboratory system having a diagnostic laboratory device; obtaining a grid for the diagnostic laboratory system, the grid having units comprising diagnostic laboratory devices and one or more tracks connecting the diagnostic laboratory devices; distributing an intelligent agent to each sample carrier; determining a target for each agent assigned to the sample carrier holding the sample container, the target including an earliest arrival time constraint, a deadline constraint, and a target processing time constraint; and employing a multi-agent path finding (MAPF) trained neural network to determine the action of each agent, the neural network being trained with arrival, deadline and priority constraints, and non-instantaneous target processing times. Numerous other aspects are provided.

A method for integrating images of a tube assembly includes capturing images of a tube assembly to generate a captured image. The captured image is decomposed into a plurality of features in a latent space using a trained image decomposition model. One or more of the features in the latent space are manipulated into one or more manipulated features. An integrated tube assembly image is generated having at least one of the manipulated features using a trained image synthesis model. Other methods and systems are disclosed.

In some embodiments, a staffing recommendation tool for a diagnostic laboratory is provided that includes a processor and a memory coupled to the processor. The memory includes computer program instructions that, when executed by the processor, cause the processor to receive local staff-related data for a local diagnostic laboratory, wherein the local staff-related data includes at least one of workload, laboratory schedule, available equipment, tasks to be performed, staff roles, and job categories for the local diagnostic laboratory; access a global database to obtain external staff-related data for one or more other diagnostic laboratories relevant to the local staff-related data; employ the local staff-related data and the external staff-related data to determine staffing recommendations for the local diagnostic laboratory; and output the staffing recommendations for the local diagnostic laboratory. Numerous other aspects are provided.

A method for visualizing the status of a diagnostic laboratory system includes receiving computer-readable data at a system controller, the computer-readable data including analyzers included in the diagnostic laboratory system and test requirements for types and quantities of tests to be performed on samples by the analyzers, wherein each of the analyzers includes consumable items and maintenance items. The method also includes determining, via a time-out estimation module executed on the system controller, an estimated time-out for the analyzer based on the test requirements and the status of the consumable items and maintenance items. As with other aspects, a diagnostic laboratory system with time-out estimation is disclosed.

A method of operating a diagnostic laboratory system for analyzing biological samples is provided. The method includes providing a track within the diagnostic laboratory system, the track extending between a plurality of instruments, and providing a plurality of sample carriers movable on the track; and modeling the track in software as a plurality of blocks, each block including a movement pattern limiting the number of sample carriers within the block and indicating permitted directions in which the sample carriers may move into and out of the block. The method includes communicating availability in a first block and then, in response to the communicated availability, moving the sample carrier from a second adjacent block to the first block. Other methods and systems are also disclosed.

A method of grasping a container in a diagnostic laboratory system using a robot includes capturing one or more images of the container in the diagnostic laboratory system, where each captured image includes image data. The image data is analyzed to identify one or more surface properties of the container. A gripping position of a gripper of a robot in the diagnostic laboratory system on the container is determined in response to the analysis. The container may then be gripped by the gripper at a gripping position. Other methods and systems are disclosed.

A method of synthesizing an image of a tube assembly includes capturing an image of the tube assembly, wherein the capturing generates a captured image. The captured image is decomposed into a plurality of features in latent space using a trained image decomposition model. One or more of the features in the latent space is manipulated into one or more manipulated features. A synthesized tube assembly image is generated with at least one of the manipulated features using a trained image composition model. Other methods and systems are disclosed.

A method of determining the accuracy of a test performed by a diagnostic laboratory system includes obtaining one or more first measurements during a first operation of the test performed by the diagnostic laboratory system. One or more second measurements are obtained during a second operation of the test performed by the diagnostic laboratory system. The first measurements and the second measurements are collectively analyzed using a trained model that calculates an uncertainty score for the test based on learned correlations between the first operation and the second operation. The uncertainty score may be used to determine whether the test results can be relied upon or whether the test should be rerun. Other methods and systems are disclosed.

A method of creating datasets for training a machine-learning-based sample container detector and classifier for a diagnostic laboratory system includes receiving a raw dataset of training data including a plurality of images of sample containers having different properties; receiving a clean annotation subset of the raw dataset; training an annotation cleaning model using the clean annotation subset, wherein once trained, the annotation cleaning model is configured to annotate images of sample containers; running the raw dataset through the trained annotation cleaning model to generate a clean annotation dataset; applying a clustering algorithm to the clean annotation dataset to identify errors in labels in the clean annotation dataset; correcting identified label errors in the clean annotation dataset so as to generate a clean annotation, clean label dataset. The method may further include training a sample container detector and classifier using the clean annotation, clean label dataset. Numerous other embodiments are provided.

In some embodiments, a diagnostic laboratory system includes a plurality of diagnostic laboratory instruments; an inventory storage module separate from the diagnostic laboratory instruments and configured to store inventory for one or more of the diagnostic laboratory instruments; and a transport system configured to move samples between the plurality of diagnostic laboratory instruments and inventory between the inventory storage module and one or more of the diagnostic laboratory instruments. Numerous other aspects are provided.

A method of updating training of an annotation generator of a diagnostic laboratory system includes providing an imaging device in the diagnostic laboratory system, wherein the imaging device is controllably movable within the diagnostic laboratory system; capturing a first image within the diagnostic laboratory system using the imaging device, the first image captured with at least one imaging condition; performing an annotation of the first image using the annotation generator to generate a first annotated image; and updating training of the annotation generator using the first annotated image. Other methods and systems are disclosed.

This invention provides a Python-based automated diagnostic method and control system for space electric propulsion, relating to the field of space electric propulsion technology. The method includes: responding to an import operation of a Python function, displaying the imported Python function in the GUI of a host computer; responding to a configuration operation of preset variables, establishing a communication connection between the host computer and hardware instruments based on the preset variables, the hardware instruments including the space electric propulsion system to be diagnosed and its diagnostic equipment; responding to a configuration operation of a diagnostic script, adding the target Python function from the imported Python function to a specified location in the GUI, and having the GUI run to generate an automated diagnostic script; and responding to a running operation of the automated diagnostic script, running the automated diagnostic script on the hardware instruments to obtain the diagnostic experimental results of the space electric propulsion system. This invention can significantly simplify the multi-condition performance diagnostic test process for electric thrusters.

A method of grasping a container in a diagnostic laboratory system using a robot includes capturing an image of a sample container to be used in a diagnostic laboratory system, wherein the captured image includes image data; executing a machine learning model to analyze the image data and locate one or more semantic keypoints on the image of the sample container; determining a grasping location on the sample container at least partially based on the locations of the one or more semantic keypoints; and generating instructions to direct a gripper of a robot to grasp the sample container at the grasping location. Other methods and systems are disclosed.

Diagnostic laboratory distribution system. The invention describes a diagnostic laboratory distribution system, wherein the distribution system comprises a plurality of carriers, wherein the carriers are adapted to carry one or more items. The distribution system comprises a transport plane, wherein the transport plane is adapted to support the carriers, and drive means, wherein the drive means are adapted to move the carriers on the transport plane, and a control device for controlling the drive means. The distribution system comprises a cover for the transport plane, and a humidity sensor connected to the control device and an air flow generating device connected to the control device. The air flow generating device is distributed over the distribution system to generate an air flow between the cover and the transport plane. The control device is configured to activate the air flow generating device in case the humidity sensor measures a humidity above a predetermined threshold.

In some embodiments, a method is provided that includes creating a simulation model of spectral response of a sample container and a sample contained within the sample container. The method further includes employing the simulation model to generate a plurality of simulated spectral responses based on at least one of different sample container properties and different sample properties. The method also includes generating a training dataset based on the plurality of simulated spectral responses, and training, via a processor, a machine-learning model using the training dataset. The method further includes selecting at least a portion of the trained machine-learning model for use in a diagnostic laboratory system. Numerous other aspects, methods, and systems are disclosed.

A method of determining accuracy of a test performed by a diagnostic laboratory system includes obtaining one or more first measurements during a first operation of a test performed by the diagnostic laboratory system. One or more second measurements are obtained during a second operation of the test performed by the diagnostic laboratory system. The first measurements and the second measurements are jointly analyzed using a trained model that computes an uncertainty score for the test using a correlation based on learning between the first operation and the second operation. The uncertainty score can be used to determine whether a test result is reliable or whether the test should be re-run. Other methods and systems are disclosed.

A method for operating an in-vitro-diagnostics laboratory system (IVD) laboratory system. The IVD laboratory system has a housing (12) with an opening (12a); an actuator device (10), arranged in the housing (12), for processing sample containers; a cover (11) configured to cover the opening (12a); a cooling device (14) configured to cool the sample containers; and a cooling device control unit (15) configured to control operation of the cooling device (14). The method comprises: determining whether the cover (11) is open; determining whether the cooling device control unit (15) is active; in a normal system mode, disabling operation of the actuator device (10) based on at least one of the cover (11) being open and the cooling device control unit (15) being inactive; and in a bypass system mode, enabling operation of the actuator device (10) based on the cover (11) being open and the cooling device control unit (15) being active.

The present disclosure relates to a method for operating an in vitro diagnostic (IVD) laboratory system, the IVD laboratory system including a housing (12) with an opening (12a), an actuator device (10) disposed within the housing (12) for processing sample containers, a cover (11) configured to cover the opening (12a), a cooling device (14) configured to cool the sample containers, and a cooling device control unit (15) configured to control operation of the cooling device (14). The method includes determining whether the cover (11) is open, determining whether the cooling device control unit (15) is activated, disabling operation of the actuator device (10) in a normal system mode based on at least one of the cover (11) being open and the cooling device control unit (15) not being activated, and enabling operation of the actuator device (10) in a bypass system mode based on the cover (11) being open and the cooling device control unit (15) being activated.

The present invention belongs to the technical field of laser-matter interaction, and in particular relates to a multimodal intense continuous laser ablation diagnostic method. The method comprises: S1: utilizing an ablation diagnostic experimental platform to obtain in real time the laser spectrum incident on the target material to be diagnosed; S2: obtaining the continuous spectrum, pump peak, and laser scattering peak; S3: calculating in real time the weights of the normalized continuous spectrum, normalized pump peak, and normalized laser scattering peak; S4: plotting a normalized continuous spectrum weight curve, a normalized pump peak weight curve, and a normalized laser scattering peak curve in a weight change curve graph based on the normalized continuous spectrum weight, normalized pump peak weight, and normalized laser scattering peak weight calculated in real time in step S3; and S5: selecting an ablation diagnostic criterion based on the changing trends of each curve in the weight change curve graph to obtain an ablation diagnostic result for the target material to be diagnosed. The present invention can solve the problem of remote diagnosis of laser ablation targets.

An optical probing system for use in a diagnostic laboratory system includes an imaging location at which a sample container is positioned, an optical source configured to generate an optical beam, and a first lens configured to receive the optical beam from the optical source and to generate a collimated beam. A second lens has a focal position within the imaging location and is configured to receive the collimated beam and focus the collimated beam at the second lens' focal position. A third lens has a focal position that coincides with the focal position of the second lens, a fourth lens is configured to receive the optical beam from the third lens, and an optical detector is configured to receive the optical beam from the fourth lens. Numerous other embodiments are provided.

A sample handler of a diagnostic laboratory system includes a plurality of holding locations configured to receive sample containers. An imaging device is movable within the sample handler and is configured to capture images of the holding locations and sample containers received therein. A controller is configured to generate instructions that cause the imaging device to move within the sample handler and capture images. A classification algorithm is implemented in computer code, and includes a trained model configured to classify objects in the captured images. Other sample handlers and methods of handling sample containers are disclosed.

Diagnostic laboratory systems provided herein employ a machine learning software model to identify locations of sample containers and empty container slots in different types of sample container carriers. The model training data is based on images of different sample container carrier types each having at least two sample containers and at least one empty container slot. The images are overlaid with an estimated grid of slots based on identified locations of the at least two sample containers in the image and at least one pre-determined grid parameter. Image patches are extracted from the images based on the estimated grid. Each image patch includes a sample container or an empty container slot upon which locations of sample containers and empty container slots can be identified in sample container carriers received in a diagnostic laboratory system. Systems and methods of training a model and operating a diagnostic laboratory system are disclosed.