Abstract: Methods of characterizing a sample container or a biological sample in an automated diagnostic analysis system using an artificial intelligence (AI) algorithm include retraining of the AI algorithm in response to characterization confidence levels determined to be unsatisfactory. The AI algorithm is retrained with data (including image data and/or non-image data) having features prevalent at the site where the automated diagnostic analysis system is operated, which were not sufficiently or at all included in training data used to initially train the AI algorithm. Systems for characterizing a sample container or a biological sample using an AI algorithm are also provided, as are other aspects.

Abstract: Methods of operating a diagnostic laboratory system include providing a module configured to perform a function on an item in the diagnostic laboratory system; providing a plurality of sensors, each of the plurality of sensors configured to monitor the function or the item and generate sensor data in response to the monitoring; checking an operational status of a first sensor of the plurality of sensors; receiving sensor data from at least one of the plurality of sensors; and scaling sensor data from the first sensor in response to the operational status and the sensor data to generate revised sensor data. Systems including a middleware server configured to carry out the methods are provided as are other aspects.

Abstract: An analyzer system for in vitro diagnostics includes a sample handler module having a robot arm that delivers samples from drawers into carriers on a linear synchronous motor automation track. Samples are delivered via the automation track to individual track sections associated with individual analyzer modules. Analyzer modules aspirate sample portions directly from the sample carriers and perform analysis thereon.

Abstract: A method of determining a 3D center location of a specimen container on a track. The method includes providing a calibration object on the track; providing an initially calibrated image capture device adjacent to the track; moving the calibration object to at least two different longitudinal positions along the track; capturing a first image with the calibration object located at the first longitudinal position; capturing a second image with the calibration object located at the second longitudinal position; and determining a three-dimensional path trajectory of a center location along the track based at least upon the first image and the second image. The method can be used to determine a 3D center location of a specimen container imaged anywhere within a viewing area. Characterization apparatus and specimen testing apparatus adapted to carry out the methods are described, as are other aspects.

Abstract: A method of aligning a component to a structure in a diagnostic laboratory system. The method includes aligning a position sensor to the structure; sensing a position of the component using the position sensor; and calculating the position of the component relative to the structure based at least in part on the sensing. Other methods, apparatus, and systems are disclosed.

Abstract: A method of operating a diagnostic instrument includes illuminating an imaging location of the diagnostic instrument with first light having a first spectrum for a first period and capturing a first image of the imaging location illuminated by the first light. The method further includes illuminating the imaging location of the diagnostic instrument with second light having a second spectrum for a second period, the second spectrum being more destructive to a chemical configured to be received in the diagnostic instrument than the first spectrum; and capturing a second image of the imaging location illuminated by the second light. Other methods and diagnostic instruments are disclosed.

Abstract: A method of characterizing a specimen to be analyzed in an automated diagnostic analysis system provides a segmentation determination and/or an HILN (hemolysis, icterus, lipemia, normal) determination of the specimen while providing characterization training updates based on the accuracy and/or confidence in the determinations. The method includes identifying an incorrect or low confidence segmentation or HILN determination, forwarding the incorrect or low confidence determination from the HILN network to a database, and providing one or more training images to the HILN network based on the incorrect or low confidence determination. Quality check modules and systems configured to carry out the method are also described, as are other aspects.

Abstract: A method of characterizing a specimen and specimen container to be analyzed in an automated diagnostic analysis system. The method can provide a segmentation determination and/or an HILN determination (hemolysis, icterus, lipemia, or normal) of the specimen while protecting patient information. The method includes capturing an image of a specimen container via an image capture device, identifying a label affixed to the specimen container in the captured image via an anonymization network, and editing the captured image via the anonymization network to mask some or all information present in the label so that it is removed from the captured image. Quality check modules and systems configured to carry out the method are also described, as are other aspects.

Abstract: A method of characterizing a specimen to be analyzed in an automated diagnostic analysis system provides an HILN classification (hemolysis, icterus, lipemia, normal) of the specimen along with a basis for that determination. The method includes assigning a hash code to each training image of a sample specimen used in the characterization training process. In response to an HILN determination for a test specimen, the method can retrieve via the hash code one or more of the closest matching training images upon which the HILN classification is based. The one or more of the closest matching training images can be displayed alongside of the one or more images of the test specimen. Quality check modules and systems configured to carry out the method are also described, as are other aspects.

Abstract: A calibration method is provided including identifying the imaging area on each light panel with respect to each imaging device. A center position of the imaging area of each light panel for each imaging device is determined. An optimal optical center of the imaging apparatus using the center position of the imaging area of each imaging device is determined. A tube calibration tool is installed in a carrier on a track, and the carrier is moved on the track so that a center of the tube calibration tool is located at a closest location to the optimal optical center of the imaging apparatus. The center of the tube calibration tool is used to determine a center of a region of interest (ROI) for backlight calibration. Methods for heath checking the calibration and apparatus used to carry out the calibration are provided as well as other aspects.

Abstract: A method of aligning a component to a structure in a diagnostic laboratory system. The method includes aligning a position sensor to the structure; sensing a position of the component using the position sensor; and calculating the position of the component relative to the structure based at least in part on the sensing. Other methods, apparatus, and systems are disclosed.

Abstract: A method of determining a 3D center location of a specimen container on a track. The method includes providing a calibration object on the track; providing an initially calibrated image capture device adjacent to the track; moving the calibration object to at least two different longitudinal positions along the track; capturing a first image with the calibration object located at the first longitudinal position; capturing a second image with the calibration object located at the second longitudinal position; and determining a three-dimensional path trajectory of a center location along the track based at least upon the first image and the second image. The method can be used to determine a 3D center location of a specimen container imaged anywhere within a viewing area. Characterization apparatus and specimen testing apparatus adapted to carry out the methods are described, as are other aspects.

Abstract: Methods of predicting a fault in a diagnostic laboratory system include providing one or more sensors; generating data using the one or more sensors; inputting the data into an artificial intelligence algorithm, the artificial intelligence algorithm configured to predict at least one fault in the diagnostic laboratory system in response to the data; and predicting at least one fault in the diagnostic laboratory system using the artificial intelligence algorithm. Other methods, systems, and apparatus are also disclosed.

Abstract: Apparatus for robotic arm alignment in an automated sample analysis system includes a robotic arm, a sample tube carrier, a plurality of optical components (including, e.g., one or more cameras), and a controller. The controller is operative to process images received from the optical components to determine a first set of coordinates of a first marker relative to the sample tube carrier and determine a second set of coordinates of a second marker relative to the robotic arm. The controller is further operative to adjust the position of the robotic arm and/or the sample tube carrier in response to an excessive offset between the first and second sets of coordinates. In some embodiments, a positioning tool includes the first and second markers thereon. Methods of robotic arm alignment with a sample tube carrier in an automated sample analysis system are also provided, as are other aspects.

Abstract: An analyzer system for in vitro diagnostics includes a sample handler module having a robot arm that delivers samples from drawers into carriers on a linear synchronous motor automation track. Samples are delivered via the automation track to individual track sections associated with individual analyzer modules. Analyzer modules aspirate sample portions directly from the sample carriers and perform analysis thereon.

Abstract: A method of characterizing a serum or plasma portion of a specimen in a specimen container provides a fine-grained HILN index (hemolysis, icterus, lipemia, normal) of the serum or plasma portion of the specimen, wherein the H, I, and L classes may each have five to seven sub-classes. The HILN index may also have one un-centrifuged class. Pixel data of an input image of the specimen container may be processed by a deep semantic segmentation network having, in some embodiments, more than 100 layers. A small front-end container segmentation network may be used to determine a container type and boundary, which may additionally be input to the deep semantic segmentation network. A discriminative network may be used to train the deep semantic segmentation network to generate a homogeneously structured output. Quality check modules and testing apparatus configured to carry out the method are also described, as are other aspects.

Abstract: Methods of identifying a defect in a machine vision system. Embodiments of the method include providing a first imaging device having a first field of view; moving a reflective tool through the first field of view; capturing a plurality of images of the reflective tool at different locations in the first field of view using the first imaging device; and analyzing at least one of the plurality of images to identify one or more defects in the machine vision system. Systems and apparatus configured to carry out the methods are provided, as are other aspects.

Abstract: A method of characterizing a serum or plasma portion of a specimen in a specimen container provides an HILN (hemolysis, icterus, lipemia, normal) determination. Pixel data of an input image of the specimen container is processed by a classification network to identify whether the specimen contains plasma or serum. Pixel data representing a plasma sample are forwarded to a segmentation/classification/regression network trained with plasma samples for HILN determination. Pixel data representing a serum sample are forwarded to a transformation network, wherein the serum sample pixel data is transformed into pixel data that matches pixel data of a corresponding previously-collected plasma sample by changing sample color, contrast, intensity, and/or brightness. The transformed serum sample pixel data are forwarded to the segmentation/classification/regression network for HILN determination. Quality check modules and testing apparatus configured to carry out the method are also described, as are other aspects.

Abstract: A quality check module for characterizing a specimen and/or a specimen container. The quality check module includes an imaging location within the quality check module configured to receive a specimen container containing a specimen, one or more cameras located at one or more viewpoints adjacent to the imaging location, and one or more spectrally-switchable light source including a light panel assembly located adjacent the imaging location and configured to provide lighting for the one or more cameras, the spectrally-switchable light source configured to be operatively switchable between multiple different spectra. Methods of imaging a specimen and/or specimen container and specimen, and specimen testing apparatus including a quality check module adapted to carry out the method are described herein, as are other aspects.

Abstract: A quality check module for characterizing a specimen and/or a specimen container including stray light compensation. The quality check module includes an imaging location within the quality check module configured to receive a specimen container containing a specimen, one or more image capture devices configured to capture images of the imaging location from one or more viewpoints, and one or more light sources configured to provide back lighting for the one or more image capture devices, and one or more stray light patches located in an area receiving stray light from the one or more light sources enabling stray light affecting the images to be compensated for and to provide a stray light compensated image. Calibration methods, methods of characterizing a specimen, specimen testing apparatus including a quality check module, and specimen container carriers including one or more stray light patches are provided, as are other aspects.