Abstract: 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.

Abstract: An optical characterization apparatus for imaging a specimen container containing a specimen. The optical characterization apparatus includes a moveable hood configured to move between an open state and a closed state relative to a specimen container imaging location and having an interior, wherein when the moveable hood is in the closed state, a specimen container positioned at the specimen container imaging location is at least partially located within the interior of the moveable hood. One or more optical devices coupled to or within the interior of the moveable hood are positioned, when the moveable hood is in the closed state, to allow imaging of a specimen container positioned at the specimen container imaging location. Automated specimen testing systems, optical characterization apparatus, and methods of measuring characteristics of specimen containers are provided, as are other aspects.

Abstract: A method of calibrating an imaging device adapted to characterize a feature of a sample container, such as a cap color or cap type. The method includes providing a calibration tube including an imaging surface at an imaging location of a first imaging apparatus; illuminating the imaging surface with light emitted from multiple front light sources; adjusting a drive current to each of the multiple front light sources to establish a substantially uniform intensity of the imaging surface; recording drive current values for the multiple front light sources; replacing the calibration tube with a calibration tool having a calibration surface of a known reflectance; and measuring target intensity values of the calibration tool at the respective drive current values. Calibration tools, imaging apparatus, quality check modules, and health check methods are provided, as are other aspects.

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: 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 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.

Abstract: A method of training a sample container identification network of a diagnostic laboratory system includes obtaining a plurality of data subsets, wherein each data subset is smaller than a full training data set used to train the sample container identification network and includes a plurality of images of one or more sample containers. The sample container identification network is trained on each of the plurality of data subsets to generate a plurality of trained sample container identification networks. Each of the trained sample container identification networks are testing using testing data that includes test images of sample containers, wherein the testing includes identifying the sample containers in the test images. A core data set is selected from one of the plurality of data subsets based on the testing. the core data set for use in training a deployed sample container identification network. Other methods and systems are disclosed.

Abstract: A method of characterizing a specimen as containing hemolysis, icterus, or lipemia is provided. The method includes capturing one or more images of the specimen, wherein the one or more images include a serum or plasma portion of the specimen. Pixel data is generated by capturing the image. The pixel data of the one or more images of the specimen is processed using a first network executing on a computer to predict a classification of the serum or plasma portion, wherein the classification comprises hemolysis, icterus, and lipemia. The predicted classification is verified using one or more verification networks. Quality check modules and specimen testing apparatus adapted to carry out the method are described, as are other aspects.

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 imaging a sample container and/or a specimen in a sample container. The method includes enclosing at least a portion of a sample container with a moveable hood, the moveable hood having a wall with one or more openings extending between an interior of the moveable hood and an exterior of the moveable hood. Image data of the sample container is generated using one or more imaging devices positioned exterior to the moveable hood. The one or more imaging devices have a line of sight to the sample container through the one or more openings. Automated specimen testing systems, optical characterization apparatus, and methods of measuring characteristics of sample containers are provided, as are other aspects.

Abstract: 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.

Abstract: A method of training a model of a diagnostic apparatus includes providing one or more first tube assemblies of a first type and one or more second tube assemblies of a second type in a diagnostic apparatus; capturing one or more first images of at least a portion of each of the one or more first tube assemblies and the second tube assemblies using the imaging device. Training the model includes identifying tube assemblies of the first type and tube assemblies of the second type based on the one or more first images and the one or more second images. Tubes assemblies of the first type are grouped into a first group and tube assemblies of the second type are grouped into a second group. Other methods and apparatus are disclosed.

Abstract: In some embodiments, a method of determining a viewpoint for optically inspecting a sample within a sample container is provided that includes (a) employing a sensor to capture image data of a sample container including a sample, wherein a portion of the sample container includes a label having first and second ends; (b) rotating at least one of the sample container and the sensor about a central axis of the sample container so that the sensor capture s image data including at least the first and second ends of the label; (c) employing the captured image data to generate an unwrapped image of the sample container; (d) processing the unwrapped image to characterize the label and produce label characterization information; and (e) employing the label characterization information to identify a viewpoint through which to optically inspect the sample within the sample container. Numerous other aspects are provided.

Abstract: 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.

Abstract: 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.

Abstract: A method of identifying a tube type. The method includes capturing one or more pixelated images of a cap affixed to a tube; identifying a color of one or more pixels of the pixilated image of the cap; identifying one or more gradients of a dimension of the cap; and identifying the tube type based at least on: the color of the one or more pixels, and the one or more gradients of a dimension of the cap. Apparatus adapted to carry out the method are disclosed as are other aspects.

Abstract: 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.

Abstract: An automated diagnostic analysis system includes a plurality of modules for processing and analyzing biological samples, a sample transport system for transporting sample containers to and from each of the modules, and a system controller for workflow planning and execution of sample analyses. To increase functionality of the automated diagnostic analysis system without increasing the floor space of the system, one or more of the modules may be configured to receive one or more functional units each operative to perform an additional action in connection with the sample analyses. The functional unit does not require separate access to the sample transport system, and the system controller is configured to dynamically include the functional unit in the workflow planning and execution of the sample analyses. Methods of operating an automated diagnostic analysis system are also provided, as are other aspects.

Abstract: 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.

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.