Analytical sample container classification

Abstract

To assess, prior to admittance into an analytical system, imperfections or problems of a visual identifier assigned to a sample container.SOLUTION: A computer implemented method includes: acquiring a digital representation of a visual identifier (3A) associated with a sample container (1); identifying at least one device included in an analytical system intended to perform at least one analytical testing using the sample container (1), and including an optical identifier reader; classify the sample container (1) associated with the analytical identifier (3A) by characterizing an ability of the optical identifier reader to decode the visual identifier (3A) associated with the sample container (1) to thereby generate a corresponding classification result characterizing the sample container (1) associated with the visual identifier (3A); and outputting a message defining the classification result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to computer-implemented methods for classification of analytical sample containers, as well as related systems, apparatus, computer-implemented methods, computer program elements, and computer program products for training classifiers of analytical sample containers with visual identifiers. [Background technology]

[0002] In vitro diagnostic (IVD) analytical laboratories can analyze a large number of patient samples. Each sample is isolated in an analytical sample container before entering the IVD laboratory. The sample can potentially contain at least one analyte of interest, such as a molecule, ion, protein, metabolite, pathogen, etc. Typically, one of the tasks of an IVD test is to detect the presence and / or concentration of one or more analytes in a sample. More generally, an IVD test can refer to characterizing the biological characteristics of a sample. An IVD test can include performing at least one analytical test on a sample, which can allow conclusions to be drawn regarding the biological characteristics of the sample. An analytical test can include, for example, the addition of a reagent to the sample, a possible detectable reaction between the sample and the reagent, and / or the detection or non-detection of this reaction. Detection of the reaction can include measuring physical values ​​of the sample (or a complex obtained by using the sample, such as a sample-reagent mixture), such as the spectrum and / or intensity of radiation reflected by and / or transmitted through the sample (or a complex obtained by using the sample).

[0003] IVD laboratories are complex and custom-designed based on each laboratory's requirements. A typical IVD laboratory processes thousands or even tens of thousands of analytical sample containers per day. Therefore, analytical sample container identification cannot be effectively performed without automated means, such as attaching visual identifiers, such as barcodes or QR codes, to sample containers. While barcodes and QR codes have error correction mechanisms, they are still susceptible to improper application or damage during transportation. Damage to the visual identifiers on sample containers entering an IVD laboratory can lead to a variety of errors that can only be addressed manually. Therefore, there is room for further improvement in sample container identification techniques. Summary of the Invention

[0004] According to a first aspect, there is provided a computer-implemented method for sorting analytical sample containers, the method comprising: obtaining a digital representation of a visual identifier associated with the sample container; - identifying at least one device included in an analytical system intended to perform at least one analytical test using the sample container, the device comprising an optical identifier reader and / or a camera; characterizing the ability of an optical identifier reader and / or camera of at least one device included in the analytical system to read the visual identifier associated with the sample container, thereby classifying the sample container associated with the visual identifier, thereby generating a corresponding classification result characterizing the sample container associated with the visual identifier; -Output a message that defines the classification result Includes:

[0005] An effect is that sample containers having associated visual identifiers can be evaluated for defects or problems with the visual identifiers used to identify the sample containers before they are accepted into an analytical system. The analytical system may include various analytical equipment and requires the sample containers to be identified before tests are performed on the sample containers as part of a workflow. If the visual identifier is damaged, poorly printed, or improperly applied to the sample container, at least one device included in the analytical system may not be able to properly decode the visual identifier.

[0006] Currently, if a sample container has a visual identifier that cannot be decoded by at least one device included in the analytical system required to execute the associated test order, the acceptance of the sample container into the analytical system creates significant productivity issues. Currently, the best-case scenario is to reroute the unidentifiable sample container within the analytical system to a holding station, where a skilled analytical system operator manually inspects the sample container and provides a new visual identifier. However, the process of rerouting the unidentifiable sample container in the analytical system's transport subsystem can itself cause inefficiencies in other workflows being performed by the analytical system. If the test involving the unidentifiable sample container is a STAT test, i.e., a highly urgent test, the results of the STAT test may be too late to be usable due to the time required to address the rerouted unidentifiable sample container. Furthermore, large systems can incur significant inefficiencies due to damaged or unidentifiable visual identifiers, requiring numerous additional staff members to address the unidentifiable sample container. In the worst-case scenario, the presence of an unidentifiable sample container in an analytical device within the analytical system could require the entire analytical system to be paused while the unidentifiable sample container is removed.

[0007] Thus, by performing classification of analytical sample containers according to the first aspect, it becomes possible to compare in advance the visual identifier associated with each sample container with the capabilities of one or more analytical devices included in the analytical system to which the sample container is sent.

[0008] In one example, a faulty visual identifier can be detected at a collection location where a sample container is first filled with a sample obtained from a patient. Collection locations include, for example, a GP or home environment, as well as remote settings such as a clinical site. This means that a clinician or laboratory technician can correct the visual identifier problem before the sample container is sent to a central laboratory equipped with an analytical system. For example, a mobile phone application can be used to scan a sample container equipped with a visual identifier, and the mobile phone application can provide feedback to the clinician regarding whether the selected central laboratory can process the desired test order based on a visual analysis of the visual identifier, for example, obtained by the mobile phone camera.

[0009] In one example, a label printer at a collection site where a patient sample is obtained may be defective and unable to generate the visual identifier associated with the sample container into the visual identifier required for a particular test order, in which case this fact can be discovered in advance and the sample container can be marked for relabeling upon arrival at a central laboratory containing an analytical system.

[0010] In another example, a faulty visual identifier can be detected upon receipt of the sample into the analytical system, for example, the visual identifier associated with the sample container may be in good condition when it leaves the clinic but may be damaged during transport.

[0011] In another example, the visual identifier may be measured at one or more points or nodes within the analytical system, for example, the visual identifier may be damaged in a central laboratory, and problems caused by such damage may be detected and corrected in advance.

[0012] Therefore, preventing problems caused by damage to visual identifiers associated with sample containers leads to increased throughput of the overall analytical system or reduced downtime of the overall analytical system. On average, urgent tests are less likely to incur delays due to downtime caused by damage to visual identifiers associated with sample containers. On average, laboratory personnel do not have to expend as much effort correcting problems with damaged visual identifiers associated with sample containers once the sample containers enter the analytical system, because sample containers with defective visual identifiers that could cause problems can be rerouted before being accepted into the analytical system.

[0013] According to a second aspect, there is provided a system comprising: an apparatus comprising an optical identifier reader and / or camera configured to acquire a digital representation of a visual identifier associated with a sample container; an analytical system comprising at least one apparatus configured to perform at least one analytical test; a communications network; and a data processing agent communicatively connected to the apparatus and the analytical system via the communications network.

[0014] The data processing agent is configured to obtain a digital representation of the visual identifier associated with the sample container and identify at least one device included in the analytical system, the analytical system intended to perform at least one analytical test using the sample container.

[0015] At least one device comprises an optical identifier reader, and the data processing agent is further configured to characterize the ability of the optical identifier reader and / or camera of the at least one device included in the analytical system to read the visual identifier associated with the sample container, thereby classifying the visual identifier associated with the sample container, generate a corresponding classification result characterizing the visual identifier associated with the sample container, and output a message defining the classification result.

[0016] According to a third aspect, there is provided an apparatus comprising a communications interface, a processor, and a memory interface.

[0017] The processor is configured to host a data processing agent that, in use, is communicatively connected to the device and the analysis system via a communications network.

[0018] The data processing agent is configured to obtain a digital representation of the visual identifier associated with the sample container and identify at least one device included in an analytical system intended to perform at least one analytical test using the sample container. The data processing agent is further configured to characterize the ability of an optical identifier reader and / or camera of the at least one device included in the analytical system to read the visual identifier associated with the sample container, thereby classifying the visual identifier associated with the sample container, generate a corresponding classification result characterizing the visual identifier associated with the sample container, and output a message defining the classification result.

[0019] According to a fourth aspect, there is provided a computer program element comprising machine-readable instructions which, when executed, perform a computer-implemented method according to the first aspect.

[0020] According to a fifth aspect, there is provided a computer readable medium encoding a computer program element according to the fourth aspect.

[0021] According to a sixth aspect, there is provided a computer-implemented method for training a classifier of analytical sample containers with visual identifiers, the method comprising: obtaining a training set including a plurality of digital representations of visual identifiers associated with a corresponding plurality of sample containers; - labeling each digital representation in the training set with a first identifier of at least one analytical test to be performed using the sample container and, optionally, a second identifier of at least one analytical system to be used to perform the at least one analytical test; - obtaining a result set defining, for each digital representation in the training set, a determination of whether the corresponding visual identifier was correctly read by all devices of the at least one analytical system; using a machine learning process to train a classifier using the training set and the corresponding result set; Includes:

[0022] According to a seventh aspect, there is provided a computer program element comprising machine-readable instructions which, when executed, performs a computer-implemented method according to the fifth aspect.

[0023] According to an eighth aspect, there is provided a computer readable medium encoding a computer program element according to the seventh aspect.

[0024] According to a ninth aspect, there is provided a machine learning model comprising machine readable instructions to generate an output data vector in accordance with the classifier trained according to the sixth aspect when provided with an input data vector.

[0025] Optional embodiments are defined in the dependent claims to which the reader may now be referred and which are further described herein.

[0026] In this patent specification, although specific terms are used, these expressions should not be construed as being limited by the specific terms selected, but rather as relating to the general concepts behind the specific terms.

[0027] As used herein, the terms "comprises," "comprising," "includes," "including," "has," "having," or any other variation thereof, are intended to cover a non-exclusive inclusion.

[0028] In this patent specification, although specific terms are used, their expression relates to the general concepts behind the specific terms.

[0029] As used herein, reference to "at least one device included in an analytical system" encompasses any device or device component operable to perform one or more processing / workflow steps on one or more biological samples. Thus, the phrase "processing step" refers to a processing step that is physically performed, such as centrifugation, aliquoting, sample analysis, etc. The term "device included in an analytical system" also encompasses pre-analytical equipment, post-analytical equipment, analytical equipment, as well as transport elements for transferring sample containers from a first location to a second location within the analytical system.

[0030] As used herein, the term "analytical system" encompasses any monolithic or multi-modular laboratory device comprising one or more laboratory devices or operational units operable to perform analytical tests on one or more biological samples. The laboratory devices may be operatively connected to a control unit, also referred to as "laboratory middleware" or "laboratory information system."

[0031] As used herein, the terms "control unit," "laboratory middleware," or "laboratory information system" encompass any physical or virtual processing device that can be configured to control a laboratory system comprising multiple laboratory instruments in a manner that causes workflows and workflow steps to be executed by the laboratory system. The control unit may, for example, instruct the laboratory system (or particular instruments thereof) to execute pre-analytical, post-analytical, and analytical workflows / workflow steps. The control unit may receive information from a data management unit regarding which steps need to be executed on a particular sample. In some embodiments, the control unit may be integral with the data management unit, included in a server computer, and / or part of one instrument or distributed across multiple instruments in a laboratory system. The control unit may, for example, be embodied as a programmable logic controller that executes a computer-readable program with instructions to perform operations.

[0032] The term "order" or "test order" refers to a service provided to a customer of an analytical system in which a patient sample enters the analytical system in a sample container, a series of processing steps are performed on the patient sample, and the analytical device outputs a result according to the original test. In some cases, the result may be a positive or negative control and therefore may not be related to the patient sample. Thus, an "order" or "test order" is a user-identifiable identifier that is parsed by a "control unit" into one or more workflows or workflow steps required to provide a final result.

[0033] As used herein, the term "workflow" can refer to a collection of workflow steps / processing steps. According to certain embodiments, a workflow defines the sequence in which processing steps are performed. As used herein, the term "workflow step" or "processing step" encompasses any activity belonging to a workflow. Activities may be basic or complex in nature and are typically performed in or by one or more analytical instruments.

[0034] The devices included in the analytical system may comprise one or more analytical modules designed to perform respective workflows optimized for a particular type of analysis.

[0035] The devices may include analyzers for one or more of clinical chemistry, immunochemistry, coagulation, hematology, and the like.

[0036] Thus, a device may comprise one analytical module or any combination of such modules with their respective workflows, and pre-analytical and / or post-analytical modules may be connected to individual analytical modules or shared by multiple analytical modules. Alternatively, pre-analytical and / or post-analytical functions may be performed by units integrated into the device. A device may comprise functional units such as liquid handling units for pipetting and / or pumping and / or mixing of samples and / or reagents and / or system fluids, as well as functional units for sorting, storage, transport, identification, separation, and detection.

[0037] In particular, the devices in the analytical system are operable to determine parameter values ​​of a sample or its components through various chemical, biological, physical, optical, or other technical procedures. The analyzer may be operable to measure said parameters of the sample or at least one analyte and return the resulting measurements. Analysis results returned by the analyzer may include the concentration of the analyte in the sample, a digital (yes or no) result representing the presence of the analyte in the sample (corresponding to a concentration above the detection level), optical parameters, data obtained from DNA or RNA sequencing, protein or metabolite mass spectrometry, and various types of physical or chemical parameters. The analyzer may also include units that assist in pipetting, metering, and mixing of the sample and / or reagents. The analyzer may also include a reagent holding unit for holding reagents for performing the assay. The reagents may be arranged, for example, in the form of containers or cassettes containing individual reagents or groups of reagents and may be placed in appropriate receptacles or locations in a storage compartment or conveyor. A consumable supply unit may also be included. The analyzer may include a processing and detection system with a workflow optimized for a particular type of analysis. Examples of such analyzers are clinical chemistry analyzers, coagulation chemistry analyzers, immunochemistry analyzers, urine analyzers, nucleic acid analyzers, tissue analyzers (including morphological and histochemical stainers) used to detect the results of or monitor the progress of a chemical or biological reaction.

[0038] As used herein, the term "pre-analytical equipment" includes one or more laboratory devices for performing one or more pre-analytical processing steps on one or more biological samples to prepare the samples for one or more subsequent analytical tests. The pre-analytical processing steps may be, for example, centrifugation steps, capping, uncapping, or recapping steps, pipetting steps, adding buffer to the sample, etc.

[0039] As used herein, the term "post-analytical equipment" encompasses any laboratory equipment operable to automatically process and / or store one or more biological samples. Post-analytical processing steps may include re-attaching the cap, removing the sample from the analytical system, or transporting the sample to a storage unit or a unit for collecting biological waste.

[0040] The term "sample" refers to biological material suspected of containing one or more analytes of interest, the qualitative and / or quantitative detection of which may be relevant to a particular condition (e.g., a pathological state).

[0041] Samples can be derived from any biological source, such as physiological fluids including blood, saliva, eye lens fluid, cerebrospinal fluid, sweat, urine, milk, ascites, mucus, synovial fluid, peritoneal fluid, amniotic fluid, tissues, cells, etc. Samples may be pre-processed prior to use by preparing plasma from blood, diluting viscous fluids, lysing, etc., and methods of processing may include filtration, centrifugation, distillation, concentration, inactivation of interfering components, and addition of reagents. While samples may in some cases be used as obtained from their source, they may also be used following pre-processing and / or sample preparation workflows to alter the properties of the sample, e.g., after addition of an internal standard, dilution with another solution, or mixing with a reagent, to enable the performance of one or more in vitro diagnostic tests, enrich (extract / separate / concentrate) the analyte of interest, and / or remove matrix components that may interfere with the detection of the analyte of interest.

[0042] The term "sample container" refers to any individual container for transporting, storing, and / or processing a sample. In particular, but not exclusively, the term refers to laboratory glassware or plasticware, optionally with a cap on top. Often, sample containers are tubes. Sample containers are configured to accept or have associated visual identifiers that allow a user and / or device to automatically identify the sample container at any step in a workflow. In some cases, a sample container may be a flat plastic or glass slide on which a tissue sample slice is placed. In this case, the visual identifier may be a barcode, a QR code, and / or handwriting applied to the portion of the flat plastic or glass slide not bearing the tissue sample slice. Ideally, all forms of sample containers and the samples contained therein are uniquely identifiable, regardless of their form factor.

[0043] In this disclosure, a "network" refers to multiple connected devices with data communication capabilities. The connected devices in a network may be designated by being managed by a particular organization. For example, a network may be a hospital network, a laboratory network, a manufacturer's network, or a network of remote support engineers. In some embodiments, a network may consist of a set of devices forming a logical group (e.g., a network of devices for a particular organization as defined above). Additionally or alternatively, devices in a network may be located in relatively close spatial relationships (e.g., a campus, a laboratory, or a hospital building). Devices in a network may be connected by a local area network. However, in other embodiments, devices in a network may be located in two or more separate locations (e.g., two different locations in a hospital).

[0044] As used herein, the term "communications network" encompasses any type of wired or wireless network, including, but not limited to, WiFi, GSM, UMTS, or other wireless digital networks or wired networks such as Ethernet. For example, a communications network may include a combination of wired and wireless networks. Analytical device status data may be transmitted over the communications network.

[0045] The term "gateway" encompasses any hardware, firmware, and / or software-based module operable to execute program logic that enables communication with an external entity via a communications network (such as a server or another interface).

[0046] The term "server" encompasses any physical or virtual machine with a physical or virtual processor capable of accepting requests and providing responses accordingly. Those skilled in the art of computer programming will appreciate that the term machine can refer to the physical hardware itself, a virtual machine such as a Java Virtual Machine (JVM), or even separate virtual machines running different operating systems on the same physical machine and sharing the machine's computing resources. A server can run on dedicated computers, often individually referred to as "servers," or any computer with shared resources, such as a virtual server. Computers often offer several services and can operate several servers. Therefore, the term server encompasses any computerized device that shares resources with one or more client processes. A server can receive, process, and transmit analytical device status data.

[0047] The term "user interface" encompasses any suitable software and / or hardware for interaction between an operator and a machine, including, but not limited to, a graphical user interface for receiving commands as input from an operator, providing feedback, and conveying information. Also, a system / apparatus may expose several user interfaces to serve different types of users / operators. A user interface may, for example, display user feedback regarding the quality of the visual identifier and / or prompt the user to reprint the visual identifier. [Brief explanation of the drawings]

[0048] [Figure 1] 1 shows a schematic representation of two sample containers with correctly and improperly applied visual identifiers, respectively. [Figure 2] 1 shows four examples of barcode quality degradation in schematic form. [Figure 3] 1 shows a schematic diagram of an example of a system for processing a sample. [Figure 4] 1 illustrates schematically a computer-implemented method according to a first aspect; [Figure 5] 1 illustrates a schematic diagram of an example process for inspection of a sample container. [Figure 6] 1 shows a schematic data model for the inspection of sample containers. [Figure 7] 10 shows a schematic representation of a further example of a rule-based approach for processing sample vessels. [Figure 8] 10 shows a schematic diagram of an example of a graphical user interface during processing of a sample vessel. [Figure 9] 1 shows a schematic diagram of an example of machine learning classification of sample containers. [Figure 10] 1 shows a schematic diagram of an example of an apparatus according to a third aspect;

[0049] Note: The figures are not drawn to scale and are provided for illustrative purposes only, to aid in a better understanding of the invention, but not to define the scope of the invention. No limitation of any aspect of the invention should be inferred from these figures. DETAILED DESCRIPTION OF THE INVENTION

[0050] Patient samples are typically collected outside the analytical system (laboratory), at a remote collection site (such as a clinic) or in a hospital ward. Once collected, the patient sample is secured in a sample container (specimen tube) that is compatible with the automated analyzer present in the analytical system. The sample container is typically created by personnel outside the laboratory, such as a hospital phlebotomist or nurse. Automation software typically provides a unique visual identifier for each sample container. The automation software may typically include an order taking system or laboratory information system (LIS) that provides unique tube IDs as unique visual identifiers for orders that include several tube containers. The unique visual identifier is associated with the test order for the test that the laboratory staff intends to order for the patient. At the same time, the automation software creates a new record in the analytical system operator's management system, and a unique visual identifier for the sample container is generated and typically provided to a label printer. The label printer is configured to print adhesive labels as part of a print-on-demand process, which laboratory personnel apply to sample containers before transferring the sample containers for processing in the analytical system.

[0051] In an analytical system (laboratory), various pre-analytical, analytical, post-analytical, and transport means are used in an order determined by the testing workflow to analyze samples contained in sample containers and upload the results to the analytical system operator's management system. Typically, each pre-analytical, analytical, post-analytical, and transport means is equipped with an optical identifier reader capable of reading a visual identifier applied to a sample container. If the visual identifier is a barcode or QR code, for example, an optical barcode or QR code reader is used to verify each visual identifier of each sample container. In another example, a high-resolution camera can be used to determine the identification of a code printed on a label associated with a sample container.

[0052] Various manufacturers often provide different elements, such as optical means for reading visual identifiers applied to sample containers, for pre-analysis, analysis, post-analysis, and transport. In other words, different devices included in an analytical system may be equipped with different optical identifier readers (e.g., the optical identifier readers may be manufactured by different manufacturers, may have different optical configurations, or may be used in different lighting conditions). Therefore, different devices may have different capabilities for decoding improperly applied or damaged visual identifiers. A damaged visual identifier may still be successfully decoded by a subset of devices included in an analytical system, but not by some devices. Furthermore, the ability of the same device to decode a visual identifier may change over time, for example, if the sensor is subject to dust intrusion or the device experiences changes in lighting conditions.

[0053] In other words, in a given set of analytical instruments in an analytical system, a subset of the analytical instruments can read a particular visual identifier associated with a sample container, while another subset of the analytical instruments cannot read the same visual identifier with the same probability of being correctly decoded. If an analytical instrument cannot read the visual identifier, the sample is rejected and removed from the instrument (and / or transported to a holding area), and, if necessary, manually troubleshooted by laboratory staff. While such an approach places a burden on the laboratory's personnel, it is advantageous to prevent such errors as quickly as possible.

[0054] The main issues associated with the degradation of visual identifier quality are misalignment when placing the visual identifier, which can be secured by adhesive, on the sample container, poor print quality of the visual identifier due to poor maintenance of the printer infrastructure at the sample collection site, and degradation of the visual identifier during transport.

[0055] FIG. 1 shows a schematic representation of two sample containers with correctly and improperly applied visual identifiers, respectively.

[0056] The Type A sample container 1 in Figure 1 comprises a container body 4 with a tapered proximal end. The illustrated sample container 1 is a one-piece, double-bottom tube with a screw cap 2 and a tapered sample reservoir 5 at the proximal end. The sample container 1 is aligned along a longitudinal axis L. A visual identifier 3A is associated with the sample container 1. In the illustrated example, the visual identifier 3A is an alphanumeric code on an adhesive label with a barcode printed thereon.

[0057] Measured from an origin located at the proximal end of the sample container 1, longitudinal dimension X1 defines the proximal extent of the visual identifier 3A on the body of the sample container 1. Longitudinal dimension X2 defines the longitudinal dimension of the visual identifier 3A. Longitudinal dimension X3 defines the longitudinal separation between the distal extent of the visual identifier 3A on the body of the sample container 1 and the proximal extent of the cap 2. Longitudinal dimension X4 defines the longitudinal extent of the cap 2. Typically, the sample container 1 is a tube having a circular cross-section, and therefore the angular extent of the visual identifier 3A about the longitudinal axis L of the sample container 1 can also be defined.

[0058] When preparing the sample container 1, a laboratory staff member may place the printed visual identifier 3A on the side of the sample container 1 according to the alignment shown in Case A of Figure 1, which in one example may be considered the correct arrangement that allows all devices included in the analytical system to correctly decode the visual identifier 3A.

[0059] Referring to the example of Case B in FIG. 1 , visual identifier 3B is improperly applied by a laboratory staff member at an acute angle relative to the longitudinal axis L of sample container 1. This causes portions of the barcode contained in visual identifier 3B to be outside the field of view of multiple optical identifier readers used by the analytical devices of the analytical system. In other words, the oblique placement of visual identifier 3B on the outer body of sample container 1 means that no angular displacement of sample container 1 about its longitudinal axis L can fully decode visual identifier 3B, making it impossible to obtain the complete barcode of visual identifier 3B. Therefore, many optical identifier readers or cameras are unable to read visual identifier 3B.

[0060] Figure 2 shows four examples of barcode quality degradation in a simplified manner.

[0061] The above description relates to one type of visual identifier misalignment related to improper geometrical alignment of the visual identifier 3B relative to the body of the sample container 1. Typically, such improper geometrical alignment results from laboratory staff error. Figure 2 provides an example of another problem that can affect a visual identifier.

[0062] Case A in Figure 2 shows the barcode size error.

[0063] Case B of FIG. 2 illustrates the provision of barcodes with different contrast or brightness ranges.

[0064] Case C in Figure 2 shows a barcode containing vertical and / or horizontal printing errors or scratches.

[0065] Case D in Figure 2 shows a barcode affected by blurring, focus issues, or low print resolution.

[0066] According to one embodiment, the quality of the visual identifier can be evaluated by a verifier. The verifier can be used to check the quality of, for example, one-dimensional barcodes and QR codes. The verifier can check the quality of one-dimensional barcodes according to one or more of the ANSI X3.182, EN1635, and ISO15416 standards. The performance of an optical identifier reader of an analytical device included in the analytical system can also be evaluated according to one or both of the ANSI X3.182 and EN1635 standards.

[0067] Therefore, one approach to ensuring or improving the readability of visual identifiers before they enter an analytical system is to use a verifier to check the quality of the visual identifier according to one or both of the quality measures defined by the ANSI X3.182, EN 1635, and ISO 15416 standards. The grade obtained by the verifier is compared to the known reading performance of an optical identifier reader included within the intended analytical system's equipment.

[0068] The ISO 15416 standard verifier classifies 1D barcodes into five broad categories:

[0069] Grade 0: Barcode is unreadable or unscannable. A complete failure in terms of print quality. Grade 1: The barcode is partially readable but is generally considered to be of poor quality. It may not scan reliably and is not suitable for practical use. Grade 2: The barcode is of reasonable quality. It is generally readable and scannable, but may not perform optimally in all environments or with all types of scanners. Grade 3: The barcode is of good quality. It is reliably readable and scannable under most normal conditions. Grade 4: The barcode is of excellent quality. It is highly reliable and readable across a wide range of conditions and by many types of scanners.

[0070] ISO / IEC 18004:2015 is an international standard that specifies the encoding, structure, and quality of Quick Response (QR) codes. Published by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), the standard provides a comprehensive set of guidelines for creating and using QR codes. Quality-related aspects of ISO / IEC 18004:2015 relate to the following: Symbol Contrast - Measures the difference in light reflectance between the dark and light elements of a QR code. A higher contrast ratio ensures a better read. Modulation: The size ratio between the smallest and largest modules (squares) in a QR code. The modulation ratio affects the clarity of the code. Fixed Pattern Damage: Whether there is distortion in the fixed patterns of the QR Code, which are the alignment pattern and timing pattern. Distortion of these patterns can lead to decoding errors. Unused error correction capacity: QR codes typically contain error correction information that helps recover data if the code is partially damaged. Unused error correction capacity indicates a low probability of decoding errors. Print growth: How much the QR code grows or shrinks compared to the original design. Proper print growth ensures accurate scanning. Reflectance Margin: The acceptable range of reflectance values ​​for the light and dark elements of a QR code. Axial Nonuniformity: Asymmetry of the QR symbol at the edges and corners of the symbol. Grid non-uniformity: Irregularities in the grid structure of the QR code. Decoding: Whether the QR code contains the correct data when decoded.

[0071] Compliance with ISO / IEC 18004:2015 ensures consistent generation and use of QR codes, allowing them to be reliably scanned and interpreted across different devices and environments. This standard plays a key role in the widespread adoption and effective use of QR codes across a variety of industries and applications.

[0072] The quality grades of ISO / IEC18004:2015 roughly classify the quality of QR codes according to the above aspects as follows: Grade 0: The QR code is unreadable or unscannable, indicating a complete failure in terms of print quality. Grade 1: The QR code is partially readable but is generally considered to be of poor quality. It may not be scanned reliably and is not suitable for practical use. Grade 2: QR codes are generally readable and scannable, but may not work optimally in all environments or with all types of scanners. Grade 3: Good quality. The QR code is reliably readable and scannable under most normal conditions. Grade 4: Excellent quality. The QR code is highly reliable and readable across a wide range of conditions and by many types of scanners.

[0073] If all optical identifier readers contained within the analytical devices of the analytical system are able to read the visual identifier of the test subject when that visual identifier is evaluated by the verifier, then the sample container associated with the visual identifier of the test subject can be accepted into the analytical system with a high degree of confidence that all analytical devices in the analytical system will be able to read the visual identifier and therefore will not experience a system outage requiring human intervention.

[0074] Additionally, some visual identifier verifiers also check for geometric anomalies, such as those shown in Case B of Figure 1.

[0075] Additionally, aspects of visual identifier verification may be performed by obtaining an image or video of the visual identifier being inspected and performing image or video analysis on the visual identifier depicted in the image or video. Image or video analysis of the visual identifier may be performed in combination with or in lieu of the use of barcode or QR code verification hardware.

[0076] Thus, in one embodiment, the optical identifier reader's ability to read visual identifiers associated with sample vessels may be implemented using a standalone visual identifier verifier, image or video based technology, or a combination.

[0077] One example of a standalone barcode verifier is the Omron™ Microscan™ LVS-9510, although those skilled in the art will appreciate that a wide variety of verifiers from other manufacturers can be used to assess the quality of visual identifiers.

[0078] FIG. 3 shows a schematic diagram of an example of a system for processing samples.

[0079] In embodiments, system 60 is distributed across at least sample collection facility 10 and analysis system 20. In some embodiments, system 60 comprises one or more of data processing agent 30, one or more manufacturer data stores (40(n)), and analyzer monitoring agent 50.

[0080] The sample collection facility 10, or sample reception, is responsible for collecting biological samples from patients prior to analysis in the analysis system 20. The biological samples are, for example, obtained from the patient and isolated in sample containers 1 at the sample collection facility. The sample containers 1 are labeled with a visual identifier that allows subsequent identification of the sample containers 1 by the analysis system 20. In some embodiments, the sample collection facility 10 is a hospital phlebotomy department or a remote doctor's surgery or outpatient clinic. In some embodiments, the sample collection facility 10 is not located in a fixed building, but may be considered to be provided by a visiting public health nurse who can visit patients and obtain patient samples in their homes.

[0081] According to one embodiment, sample collection facility 10 may include computer 10-COMP, printer 10-PRINT configured to print visual identifiers 3A for application to one or more sample containers, optional data storage element 10-STO, and a communications gateway communicatively connected to wide area network 62. Sample collection facility 10 may further include one or both of optical identifier readers 12 and / or cameras 14 configured to obtain digital representations of visual identifiers 3A associated with sample containers 1. Optical identifier reader 12 and / or camera 14 may be operatively connected to computer 10-COMP. Computer 10-COMP may host device driver software that enables a software environment of computer 10-COMP to obtain classification results characterizing sample containers with associated visual identifiers from one or more of optical identifier readers 12 and / or cameras 14.

[0082] According to a further embodiment, pre-labeled tubes are provided. In this case, labels are not printed at the sample collection facility 10. Thus, the sample collection facility 10 does not need to include a printer 10-PRINT. Instead, the samples or tubes are pre-labeled with an identifier. The pre-labeled identifier is processed or checked as a printed visual identifier.

[0083] Additionally, computer 10-COMP is configured to host sample registration software that enables a link between a visual identifier associated with a sample container and a unique database identifier to be registered in a database hosted either at the sample registration location (10-STO) or a remote database located at an off-site data center 30. Additionally, computer 10-COMP is configured to host a device driver for printer 10-STO. For example, as part of the sample intake process, the sample registration software running on computer 10-COMP assigns a unique database identifier to the registered sample container. Computer 10-COMP may, for example, instruct printer 10-PRINT to print a unique label that includes the visual identifier, the visual identifier logically linked to the unique database identifier generated by computer 10-COMP during the sample intake process.

[0084] A healthcare professional operating computer 10-COMP and responsible for overseeing the sample intake process may, for example, affix a printed label to sample container 1. In some examples, the sample intake process may further include the healthcare professional selecting one or more test orders (order data 34) to be performed on the sample contained in sample container 1. The healthcare professional selects the one or more test orders using an interface provided by sample intake interface software on computer 10-COMP. The selected one or more test orders are logically linked to the unique database identifier associated with the sample container. Computer 10-COMP may, for example, update a remote database with the test orders assigned to each unique database identifier associated with the sample container.

[0085] Once a sample has been obtained from a patient, secured in a sample container 1, and registered with a unique database identifier logically linked to a visual identifier printed and affixed to the sample container, the historical approach has been to send one or more sample containers to an analytical system 20 (such as a central IVD laboratory) using, for example, a medical delivery service 64.

[0086] Unfortunately, however, sample containers with defective or damaged visual identifiers 3B may enter the analytical system 20 unmonitored. Some analytical devices may not be able to read such defective or damaged visual identifiers 3B, meaning that to improve efficiency, the visual identifiers must be replaced before the sample containers enter the analytical system 20. On the other hand, there may be cases where the damage to the visual identifier is minor and it can be determined that the designated analysis to fulfill a particular test order can still process the visual identifier with the minor defect. In this case, replacing the label would be inefficient.

[0087] Thus, the sample collection facility 10 comprises one or more of the optical identifier reader 12 and / or the camera 14. In one example, the camera 14 may be included in, for example, a smartphone or a smart tablet. According to one example, the functions of the computer 10-COMP, the storage means 10-STO, and the camera 14 are performed by the smartphone or smart tablet.

[0088] In embodiments where the optical identifier reader 12 is a verifier, the classification result may include, for example, an evaluation of the visual identifier according to one or more of the ANSI X3.182, EN 1635, and ISO 15416 standards. In embodiments where the optical identifier reader 12 is a camera, the classification result may include, for example, an evaluation of the visual identifier according to one or more of the ANSI X3.182, EN 1635, and ISO 15416 standards. In embodiments where the optical identifier reader 12 is a camera, the classification result may include an evaluation of the geometric arrangement of the visual identifier on a given sample container.

[0089] Before the healthcare professional hands one or more sample containers 3A to medical delivery service 64 for delivery to analytical system 20, the healthcare professional may use one or more of optical identifier reader 12 and / or camera 14 to optionally verify the quality of the visual identifier of the sample container 1 in light of the test that the healthcare professional intends to order from analytical system 20. For example, the healthcare professional may use optical identifier reader 12 (verifier) ​​and / or camera 14 to inspect sample container 1 having a label printed by printer 10-PRINT.

[0090] The computer 10-COMP compares the quality classification of the visual identifier with the registered limits of the optical identifier reader or camera included within the analytical system 20. If the computer 10-COMP receives information that at least one visual identifier does not meet the intended quality classification, the computer 10-COMP alerts a healthcare professional to this fact, so that the healthcare professional can correct the visual identifier problem before the sample container to which the visual identifier is attached is sent to the medical courier 64. For example, resolving a visual identifier defect may require applying a new label over an old label that is misaligned. In another example, resolving a visual identifier defect may require printing a new label with a different code to work around a temporarily defective pixel in the printer 10-PRINT. Printing and applying a new label to the sample container significantly reduces the likelihood that the analytical system 20 will be interrupted by a defective visual identifier.

[0091] The analytical system 20 is, for example, an in vitro diagnostic (IVD) laboratory. Processing of the sample container analytical system 20 can include, for example, receiving a plurality of sample containers with corresponding visual identifiers into the analytical system 20. The sample containers are, for example, IVD containers such as IVD tubes. The IVD containers may be held in an IVD container holder, such as an IVD tube rack. The analytical system 20 is configured to perform pre-analytical workflow steps on the samples (e.g., preliminary steps such as centrifugation). The analytical system 20 is configured to perform analytical workflow steps on the samples (e.g., adding reagents to the samples and measuring reactions between the samples and the reagents). The analytical system is configured to perform post-analytical steps on the samples (e.g., storing the samples in a refrigerator for later use).

[0092] The devices included in the analytical system 20 are typically classified according to the different types of sample processing steps they can perform. The transport IVD laboratory device 20Trans is designed, for example, to transport samples (or sample containers and / or respective holders) from one analytical device to another. The pre-analytical IVD laboratory devices 20PRE-1, 20PRE-2 are designed to perform pre-analytical steps on samples. The analytical IVD laboratory devices 20ANA-N are designed to perform analytical steps (such as analytical tests) on samples. The analytical IVD laboratory devices 20ANA-N may comprise digital analytical IVD laboratory devices designed to perform analytical calculation steps (e.g., medical algorithms). The post-analytical IVD laboratory device 20POST is designed to perform post-analytical steps and / or sample storage on samples. Some analytical devices 20 are capable of performing multiple types of sample processing steps, e.g., pre-analytical and analytical steps.

[0093] Thus, the analytical system 20 comprises one or more analytical devices 20ANA1-4 designed to process samples, for example, to perform one or more steps of an intended workflow on the samples. Sample processing can include one or more physical processing steps (e.g., moving, mixing, heating, etc.). The analytical devices 20ANA1-4 can include instrument hardware for processing the samples (e.g., grippers, reagent reservoirs, pipetting devices, heating elements, etc.), as well as instrument software designed to operate the instrument hardware. The analytical devices 20ANA1-4 can comprise a control unit designed to control, and in particular steer, the operation of the instrument hardware, and the instrument software can be designed to be executed using the control unit.

[0094] According to one embodiment, each of the analytical devices ANA1-4 includes at least one optical identifier reader or camera D1-D4. In one example, the reading capabilities of the optical identifier reader or camera D1-D4 are characterized by the optical identifier reader manufacturer according to one or more of the ANSI X3.182, EN1635, and ISO15416 standards, or equivalent standards for QR code readers.

[0095] The first analytical device ANA1 may be equipped with an optical identifier reader that has a different capability for reading visual identifiers (eg, one-dimensional barcodes and / or QR codes) compared to the other analytical devices ANA2-4 in the analytical system 20.

[0096] In one example, the reading capabilities of the optical identifier readers D1-D4 are characterized according to measurements or calibration measurements performed in situ in the analysis system 20.

[0097] In one example, the reading capabilities of optical identifier readers D1-D4 are characterized according to a drift model or machine learning model that characterizes the change over time in the decoding capabilities of each type of optical identifier reader or camera.

[0098] The analytical system 20 may further comprise a laboratory information system (LIS) communicatively connected to other devices within the analytical system 20. The LIS interfaces with or hosts middleware in the analytical system 20 configured to operate, for example, the IVD laboratory devices 20ANA-N, 20PRE-N, 20POST-N and the transport system 20Trans based on a service request or workflow generated by the LIS. According to one example, the service request or workflow is generated by the LIS based on order data 34 generated from a test order selected by a healthcare professional at the sample receiving facility 10 and logically linked to a particular sample container 1.

[0099] The LIS may collect and store or transmit sample analysis results generated from the instrument 20ANA-N. The LIS may monitor the use of reagents or consumables in the analytical system 20 and generate requests for new orders of reagents or consumables when current stocks of reagents or consumables are low. The LIS may collect information regarding maintenance needs or analyzer nonconformities and generate requests for engineering checks.

[0100] According to one embodiment, the LIS is configured to adjust the calibration or obtain capability definitions for the optical identifier readers (and / or cameras) D1-D5, DPRE, and DPOST associated with the devices of the analytical system 20. Additionally, the LIS may collect information and generate statistics defining the overall performance of the IVD laboratory. The LIS may host a resource such as an embedded website that allows laboratory and engineering staff to evaluate the performance of the IVD laboratory or any device within the IVD laboratory.

[0101] The analytical system 20 may further comprise a communications interface 20COM that enables the LIS and / or the devices 20ANA-N, 20PRE-N, 20POST-N, and transport system 20Trans of the analytical system 20 to be communicatively connected to one or more of the sample receiving facility 10, the data center 30, the set of manufacturer data stores 40(n), and / or the analyzer monitoring agent 50 via a wide area network 62.

[0102] The sample entry point of the analytical system 20 optionally includes one or both of an optical identifier reader 22 and a camera 24, which allows for verification of the visual identifier of the sample container at the entry point into the analytical system 20. For example, the sample receiving facility 10 may not include an optical identifier reader 12 and / or a suitable camera 14 that allows for obtaining classification results characterizing the visual identifier. In this case, the visual identifier of the sample container can be inspected at the entry point into the analytical system 20. Alternatively, or in addition, if a non-compliant sample container is detected by the optical identifier reader 22 and / or camera 24, a staff member at the facility hosting the analytical system 20 can forward the non-compliant sample container 1 for re-labeling before entering the analytical system 20. This approach also allows for detection of damage to the visual identifier during the medical delivery process. If a sample container containing a visual identifier does not pass the acceptance check into the analytical system 20 performed by the optical identifier reader 22 at the analytical system 20, but the same sample container has previously passed a checkout inspection performed by the optical identifier reader 12 located at the sample reception facility 10, a useful additional conclusion can be made that the visual identifier 3A was damaged during transport of the sample.

[0103] In one embodiment, the analysis system 20 may further include an internal visual identifier quality monitor 20-O. The visual identifier quality monitor 20-O may include one or more of an optical identifier reader, such as a barcode reader, and / or a camera. The purpose of the visual identifier quality monitor 20-O is to remove one or more sample containers from the transport system 20Trans of the analysis system 20 and inspect the quality of the visual identifiers of the one or more sample containers. This allows the analysis system 20 to periodically ensure that visual identifier degradation is not occurring within the analysis system 20 itself. Furthermore, if a sample container 1 with a sufficiently degraded visual identifier is discovered, the analysis system 20 may send the sample container to the automatic sample printer 20Print so that an improved-quality visual identifier can be automatically applied to the sample container (e.g., by covering the surface of the degraded visual identifier).

[0104] In one embodiment, if one or more optical identifier readers and / or cameras of analyzer system 20 are unable to satisfactorily read a visual identifier associated with a sample container, laboratory information system LIS can detect this and forward the faulty sample container to an internal visual identifier quality monitor 20-O, which captures images or an analysis of the faulty sample container and its visual identifier. In this way, examples of problematic visual identifiers can be collected from actual laboratory environments and used, for example, to train machine learning models.

[0105] According to one embodiment, the analytical system 20 further comprises a calibration reference store 20-C. The calibration reference store 20-C includes a number of placebo sample containers with visual identifiers having a set of conditions and / or a set of simulated fixed anomalies and scratches according to one of the relevant industry standards defined above. In one embodiment, the placebo sample containers may be provided by the manufacturer of one or more devices of the analytical system 20.

[0106] The purpose of the calibration reference store is to take advantage of periods of low utilization of the analytical system 20 to calibrate, or at least monitor for reader degradation of, the optical identifier readers D1-D5 in the analytical system. For example, a series of placebo sample containers with decreasing readability can be repeatedly directed to the analyzer 20ANA-3. The laboratory information system LIS collects information from the control unit of the analyzer 20ANA-3 regarding whether each placebo sample container can be correctly read and decoded. Once the calibration run is complete, the laboratory information system LIS can transmit the results to, for example, the manufacturer database 40(n) to enable maintenance planning. Alternatively, the LIS can transmit the results to the data processing agent 30SERV so that, if the optical identifier reader of the first analyzer begins to behave erratically, the data processing agent can reassign test orders (order data 34) requiring the first analyzer to an alternate analyzer. Additionally, the LIS can transmit the results of the calibration run to the analyzer monitoring agent 50.

[0107] The system 60 further comprises a data center 30 communicatively connected to at least the analyzer system 20 and the sample receiving facility 10 via a wide area network 62 .

[0108] The data center 30 includes a communication gateway 30 communicatively coupled to a data processing agent 30SERV.

[0109] The term "data processing agent" refers to a computer-implemented software module executing on one or more computing devices, such as a server, that can receive, for example, analyzer status data from the laboratory information system LIS of the analytical system 20 and sample acceptance information from the computer 10COMP of the sample acceptance facility 10. The data processing agent 30SERV may be implemented on a single server, multiple servers, and / or an internet-based "cloud" processing service such as Amazon AWS™ or Microsoft Azure™. The "data processing agent," or portions thereof, may be hosted on a virtual machine. The data processing agent can receive, process, and transmit operational information and data to the sample acceptance facility 10, the analytical system 20, the manufacturer data service 40(n), and the analyzer monitoring agent 50.

[0110] For convenience, data center 30 is illustrated as hosting data processing agent 30SERV, however, those skilled in the art will appreciate that data processing agent 30SERV may be hosted in a variety of locations, such as within analytical system 20 or at sample reception facility 10. Additionally, data processing agent 30SERV, in some embodiments, is configured to host a web application accessible by a smartphone, tablet, or computer to perform sample reception or laboratory management processes or to obtain test results.

[0111] The data center comprises a first data store 31 containing laboratory configuration information. The laboratory configuration information includes, for each of one or more analytical systems 20 available to healthcare professionals ordering tests from the sample receiving facility, records defining the system architecture of the one or more analytical systems 20. For example, the laboratory information identifies the model numbers of each piece of equipment present in the analytical systems 20 and the interconnections between the equipment.

[0112] The data center includes a second data store 32 containing visual identifier compatibility information for various analytical devices. Thus, for each device 20ANA-1 included in a given analytical system 20, the rules governing the capability definition of the optical identifier reader or camera D1 of that device 20ANA-1 are stored in the visual identifier compatibility information. The visual identifier compatibility information may be a set of general rules defined by the manufacturer of the analytical device. In some cases, the visual identifier compatibility information may be updated based on a calibration run performed using, for example, calibration standards in the calibration standard store 20-C. In some cases, the visual identifier compatibility information may be updated based on downloads or updates from one or more manufacturer data stores 40(n).

[0113] The data center includes a third data store 33 containing calibration information for one or more of the devices 20ANA-1. Calibration of the optical identifier readers D1-D5 can be performed manually by laboratory staff or automatically using systematic calibration runs from the calibration standard store 20-C. Thus, the degree of drift over time in the ability of the optical identifier readers D1-D5 to successfully read the calibration standards is registered in the third data store and can be used to more accurately predict whether a particular analyzer system will be able to successfully read a visual identifier affixed to a sample container based on the measured state of the analyzer system's optical indicia.

[0114] The data center comprises a fourth data store 34 containing order data. The order data includes a first identifier of at least one type of analytical test to be performed by the analytical system 20 using the sample container 1 and / or at least one transfer action of the sample container 1 within the analytical system 20. The order data optionally includes a second identifier for identifying the specific analytical system 20 used to perform the at least one analytical test. In other words, the order data defines the workflow step and the specific type of analyzer in which the sample container should be processed to obtain the test result.

[0115] The data center includes a fifth data store 35 containing sample intake data. This database includes patient-specific information such as the sample's unique sample container identifier linked to a (typically anonymized) code that can be used to identify the patient and the type of test or tests ordered for the sample.

[0116] Additionally, the fifth data store 35 may contain a link or database key to a results database (not shown). When a new test is ordered at the sample receiving facility, a new entry containing the sample receipt data is entered into the fifth data store 35, which includes a unique sample container identifier that is used to generate a visual identifier for the sample container.

[0117] Manufacturer data stores (40(n)) are accessible via gateway 40COM. For each type of device 20ANA-1-5, 20PRE-1, 20PRE-2, 20POST, and 20Trans in analysis system 20, the associated manufacturer may provide a device specification that includes capability definitions for the device's optical identifier reader and / or camera. An application programming interface (API) 40SERV may enable access to data store 42, which may include, for example, a set of capability definitions for various types of manufacturer equipment.

[0118] In one embodiment, the data processing agent 30SERV is configured to populate the second data store 32 with visual identifier compatibility information downloaded from at least one manufacturer data store 40(n). In another embodiment, a direct connection to the manufacturer data store 40(n) is not required and the visual identifier compatibility information can be provided to the second data store, for example, by manual entry.

[0119] The analyzer monitoring agent 50 may be hosted by a remote server or cloud service, or may be included within the data center 30 and / or the analyzer system 20. The purpose of the analyzer monitoring agent 50 is to monitor and predict changes in the types of visual identifier readers and / or cameras utilized by the devices used in the analyzer system 20. Accordingly, the analyzer monitoring agent 50 may optionally include a database 52 of visual identifier reader definitions obtained from one or more manufacturing data stores 40(n). The analyzer monitoring agent 50 may include a database 54 of successfully and unsuccessfully decoded visual identifiers obtained by the internal visual identifier quality monitor 20-O. The analyzer monitoring agent may include one or more digital models D6, D7, D8 of the visual identifier reader. Optionally, the digital models are based on machine learning models trained on information contained in the database 54 of successfully and unsuccessfully decoded visual identifiers obtained by the internal visual identifier quality monitor 20-O.

[0120] The elements of the system 60 described above are typically geographically distributed, and data communication links between the elements of the system 60 are provided, for example, by a wide area network (WAN).

[0121] FIG. 4 illustrates schematically a computer-implemented method according to a first aspect.

[0122] According to a first aspect, there is provided a computer-implemented method 80 for sorting analytical sample containers, the method comprising: obtaining 82 a digital representation of a visual identifier 3A associated with the sample container 1; - identifying 84 at least one device 20PRE-1 included in an analytical system 20 intended to carry out at least one analytical test using the sample container 1 and equipped with an optical identifier reader or camera; characterizing the ability of an optical identifier reader or camera of at least one device 20PRE-1 included in the analysis system 20 to decode the visual identifier 3A associated with the sample container 1, thereby classifying the sample container 1 with which the visual identifier 3A is associated, thereby generating a corresponding classification result 86 characterizing the sample container 1 with which the visual identifier 3A is associated; - Outputting a message that defines the classification result 88 Includes:

[0123] According to one example, the digital representation of visual identifier 3A is a digital representation of a barcode, QR code, or plain text included in visual identifier 3A. The digital representation of visual identifier 3A can include a digital image, but may also include parameters that define a visual identifier, such as a barcode or QR code, as defined in the ANSI X3.182, EN1635, and ISO15416 standards. The digital representation of visual identifier 3A can include analysis results obtained from an optical verifier to verify the quality of the barcode and / or QR code.

[0124] According to one embodiment, after obtaining a digital representation of the visual identifier 3A associated with the sample container 1, the digital representation of the visual identifier 3A is processed using an image processing algorithm to determine one or more attributes that define the quality of the visual identifier.

[0125] According to one embodiment, obtaining a digital representation of a visual identifier 3A associated with a sample container 1 includes reading the visual identifier 3A with a barcode or QR code reader to determine one or more attributes that define the quality of the visual identifier.

[0126] According to one embodiment, the one or more attributes of the visual identifier include a range of acceptable misalignment of the visual identifier relative to the longitudinal axis of the sample container; a range of acceptable occlusion of the visual identifier; a range of acceptable vertical and / or horizontal dimensions of the visual identifier; a range of acceptable blur or resolution artifacts of the visual identifier; a range of acceptable contrast or brightness ratios of the visual identifier; and / or a measure of the range of acceptable artifacts of the visual identifier, and / or the one or more attributes of the visual identifier include at least one or any combination of a barcode edge determination metric, a barcode minimum reflectance metric, a barcode minimum edge contrast metric, a barcode symbol contrast metric, a barcode modulation grade, a barcode defect grade, and / or a barcode reading grade.

[0127] For example, the digital representation of the visual identifier 3A and / or the capability definition 32 of the optical identifier reader of at least one device may characterize at least one or any combination of the following: a range of acceptable misalignment of the visual identifier relative to the longitudinal axis of the sample container, a range of acceptable occlusion of the visual identifier, a range of acceptable vertical and / or horizontal dimensions of the visual identifier, a range of acceptable blur or resolution artifacts of the visual identifier, a range of acceptable contrast or brightness ratio of the visual identifier, and / or a range of acceptable artifacts of the visual identifier.

[0128] According to one embodiment, the digital representation of the visual identifier 3A and / or the capability definition 32 of the optical identifier reader of the at least one device further characterizes at least one or any combination of a barcode edge determination metric, a barcode minimum reflectance metric, a barcode minimum edge contrast metric, a barcode symbol contrast metric, a barcode modulation grade, a barcode defect grade, and / or a barcode decodability grade.

[0129] The digital representation of the visual identifier may be a digital image of the visual identifier, or may be one or more metrics that can characterize the verifier's assessment of one or more of the aforementioned parameters. Capability definition 32, on the other hand, defines the ability of a given type of optical identifier reader to read visual identifiers that have been rated by the verifier to a given performance level.

[0130] Thus, available data sources are combined to form a prediction of sample visual identifier compatibility or effectiveness for a particular customer's laboratory equipment configuration, given the measured quality of the visual identifier.

[0131] In one example, the proposed data combination includes the following information set: laboratory configuration information (e.g., stored in a first data store 31) that characterizes, for a particular analytical system 20, the identity of the equipment available in the analytical system 20. This data is, in one example, stored in a laboratory-specific configuration file 31 in a back-end data storage solution.

[0132] The second data store 32 contains information regarding the visual identifier compatibility of each analyzer device incorporated into the analyzer system 20. In other words, the type of optical identifier reader implemented in each device present in the analyzer system 20. According to one example, this can be determined by factory testing at a custom facility using a set of samples with various barcode qualities. Alternatively, this information can be obtained from the manufacturer database 40(n).

[0133] At sample reception 10, the quality of the actual visual identifier is captured by a user front-end device, such as an optical identifier reader and / or camera located on the user's mobile phone or smart tablet. In the case of a barcode, for example, the quality of the visual identifier is based on industry standard measures such as reflectance, contrast, readability, etc.

[0134] A further data set that can be used when performing an assessment of visual identifier compatibility with the analyzer system 20 is the combination of order data 34 associated with sample containers that are equipped with visual identifiers. The analyzer system 20 may include a large number of analyzers, many of which may not be usable for a particular test order (which is automatically broken down into workflows by the laboratory information system within the analyzer system 20). Thus, if a subset of analyzers within the analysis system 20 that will function with a given visual identifier quality can be identified, sample containers with average-quality visual identifiers can still be accepted into the analysis system 20.

[0135] For example, a sample-specific test order is used to look up the corresponding device in the analytical system 20 required to analyze the specific test order. Device-specific visual identifier quality requirements are matched against the measured quality of the visual identifier to produce a classification result that characterizes the ability of each analyzer required for a given test order to decode the visual identifier. If it is determined that all devices in the analytical system 20 required to perform the specific test order can decode the specific visual identifier, the classification result is positive. If it is determined that at least one device in the analytical system 20 required to perform the specific test order cannot decode the specific visual identifier, the classification result is negative.

[0136] An additional value of indicating sample barcode quality is the ability to sort a given sample container at the sample reception 10 of a laboratory and / or reroute samples for defective sample containers depending on the quality of the visual identifier associated with the sample container. This can optimize troubleshooting of poor barcode label quality and reduce the need for relabeling within the analytical system 20. Compatibility at the sample reception 10 can be performed, for example, using a mobile device with a camera or scanner (or a mobile device communicatively connected to, for example, a barcode or QR code verifier). This provides the user at the sample reception 10 with immediate feedback regarding the ability of the downstream analyzer system intended to execute the test order to process a particular sample container with a visual identifier. Furthermore, the collected data can be used by laboratory operators to analyze the root causes of poor visual identifier quality (e.g., by collection location, identifying faulty visual identifier printers, or identifying staff training needs).

[0137] According to one embodiment, the method further comprises outputting a corresponding identifier of one or more sample vessels with a visual identifier 3A that receives a negative classification.

[0138] FIG. 5 illustrates a schematic example of a process 100 for inspection of sample containers.

[0139] Once the analytical system 20 is configured or reconfigured in step 101, a laboratory equipment configuration file 114 is generated by the designer of the analytical system 20 and provided to the data center 30. The laboratory equipment configuration file 114 defines, for example, interconnections between laboratory equipment, manufacturer identification information, etc.

[0140] For one or more devices included in the analysis system 20, device visual identifier quality parameters are obtained in step 115, for example, from the manufacturer database 40(n).

[0141] Column 102 of the process chart defines the acquisition of a test order, where a healthcare professional at the sample receiving location 10 acquires a sample from a patient and attaches a visual identifier to the sample container. A computer 10COMP at the sample receiving location 10 is used to select the test order (order data 34) intended to be performed on the sample.

[0142] Before the sample container leaves the sample reception facility 10, the sampling step 103 includes performing a scan 107 to measure the quality of the visual identifier using the visual identifier reader 12 and / or camera 14 at the sample reception location 10. Once the quality of the visual identifier has been obtained, a barcode quality check is performed in step 109 by comparing the quality of the visual identifier with the equipment visual identifier quality parameters of each analyzer device specified by the test order obtained in step 106. If the quality of the visual identifier is such that all analyzers in the analyzer system 20 required to perform the particular test order can read the visual identifier obtained in step 107, the sample container associated with the visual identifier that passes this check can be sent to the analyzer system 20. Otherwise, in step 112, the sample reception facility 10 is alerted to the issue of insufficient visual identifier quality. According to one option, the visual identifier is reprinted, and the healthcare professional can affix a new sample container and rescan the sample container to verify that the visual identifier attached to the sample container is of satisfactory quality. Another option is to re-edit the workflow to find other analyzers that can read the visual identifier and perform the associated workflow step.

[0143] Column 104 of Figure 5 shows a set of process flows that can be performed instead of or in addition to the process flow of 103. When a sample container arrives at the reception point of analyzer system 20, the quality of the visual identifier is scanned in step 108 using an optical identifier reader and / or camera. The laboratory information system LIS (for example) of analyzer system 20 consults a laboratory equipment configuration file and equipment barcode quality lookup information to determine whether the sample container with the scanned visual identifier can be forwarded to analyzer system 20 or whether the visual identifier should be replaced before the sample container is forwarded to analyzer system 20. Thus, there is an optional step 113 of providing the sample container with a new visual identifier at the collection location. If the sample container has a visual identifier of acceptable quality given the configuration of analyzer system 20 and the equipment barcode quality capabilities of the analyzers included within analyzer system 20, the sample container can be allowed to enter analyzer system 20.

[0144] FIG. 6 shows a schematic data model 70 for the inspection of sample containers.

[0145] In an exemplary setup, a specific test order and lab analyzer configuration table are provided that defines the analyzer to be used, e.g., in the form of a matrix. The combination of the analyzer to be used and the specific barcode reader capability information of the analyzer to be used defines the required barcode quality, e.g., in the form of a matrix. In addition to the required barcode quality, the specific barcode quality measured by the device to be used at the collection location defines whether it is OK or not OK for lab processing. Accordingly, an error message or OK is sent to the operator.

[0146] The data processing agent 30SERV retrieves the digital representation of the visual identifier from, for example, a data storage device 10-STO located at the sample reception facility 10. In one embodiment, the digital representation of the visual identifier is associated with a quality metric based on an industry standard for visual identifiers, e.g., acquired using an optical identifier reader and / or camera at the sample reception facility 10. In other words, the digital representation of the visual identifier characterizes the quality of the visual identifier of the test object at the sample reception facility 10. In a variant, the digital representation of the visual identifier may be acquired at the sample entry point to the analysis system 20 or from within the analysis system 20.

[0147] The data processing agent 30SERV is configured to retrieve laboratory configuration information from, for example, a first data store 31. The laboratory configuration information includes records 31A representing each functional unit included in the analytical system 20.

[0148] The data processing agent 30SERV can obtain information regarding the visual identifier compatibility of each device included in the analyzer system 20. For example, visual identifier compatibility can be determined by one or more rules that a sample container's visual identifier 3A must follow in order for the associated device to be able to read the corresponding visual identifier. A rule can define, for example, a geometric quantity, such as a limit on the angular deviation of the visual identifier relative to the longitudinal axis of the sample container. A rule can define, for example, maximum and minimum reflectance values ​​to be measured by a barcode verifier. Additional visual identifier quality metrics are described elsewhere herein, and all or any combination of such quality metrics can be used as a rule set.

[0149] The data processing agent 30SER optionally retrieves calibration information from a third data store 33 for at least one item of equipment in the analyzer system 20. The calibration information 33 may, for example, characterize how the optical detection characteristics of a typical analyzer contained in the laboratory information in the first data store 31 deviate from its typical definition.

[0150] The data processing agent 30SERV can obtain test orders 34 for tests to be performed on specific sample containers identifiable by visual identifiers.

[0151] The data processing agent 30SERV can retrieve sample accession data from a fifth data store 35. The sample accession database contains patient-specific information such as the sample's unique sample container identifier linked to a de-identified code that can be used to identify the patient and the type of test or tests ordered for the sample.

[0152] In use, the preprocessor 71 identifies that a new patient test has been ordered for a particular analyzer system 20. The particular test ordered is looked up in a set of test orders 34 (order data). The preprocessor 71 defines the laboratory that characterizes the particular analyzer system 20 from the database 31. The preprocessor 71 retrieves the order data 34 for the test that a healthcare professional is ordering for a sample, which is retrieved from the database 34. The preprocessor 71 uses the order data 34 and the laboratory configuration information in the first data store 31 to compile a workflow. The workflow defines, for example, the movement between pre-analytical or pre-processing devices, analytical devices, and post-analytical or post-processing devices within the analysis system 20.

[0153] The compilation performed by the preprocessor 71 results in a list of optical identifier readers that are used to identify visual identifiers associated with sample containers as they are processed by the analytical system 20 according to a workflow.

[0154] Comparator module 73 retrieves an associated rule set for each optical identifier reader used to identify visual identifiers according to the workflow from data store 32. According to one example, the rule sets from data store 32 may be supplemented with calibration information for each optical identifier reader of each associated device included in a particular analyzer system 20 as requested by personnel at sample receiving facility 10.

[0155] The comparator module 73 compares the representation of the visual identifier measured at the sample reception facility 10 with the associated rules defined by the workflow in the associated rule set from the data store 32. The example in FIG. 6 shows a case where seven of the eight analyzers successfully decode the visual identifier with the characteristic measured at the sample reception facility 10. However, analyzer 20ANA-3 is unable to successfully decode the visual identifier with the characteristic measured at the sample reception facility 10. If the comparison fails, a first warning message 72A is sent to the laboratory information system LIS of the analysis system 20. A second warning message 72B is sent to the computer 10COMP of the sample reception facility 10. Thus, user interface software hosted by the computer 10COMP of the sample reception facility 10 can prompt the user to address the barcode readability issue that prevented the sample container with the visual identifier that caused the comparison to fail to analyzer 20ANA-3.

[0156] In one embodiment, the data processing agent 30SERV can search the first database 31 for an alternative analytical device that can perform the test step that the analyzer 20ANA-3 cannot complete due to its inability to read the label associated with the previously identified sample container. In the illustrated case, the data processing agent 30SERV suggests the analyzer 20ANA-1 as an alternative to the analyzer 20ANA-3. The laboratory information system LIS is therefore notified in step 76 that the analyzer 20ANA-3 should be replaced with the analyzer 20ANA-1 in this workflow step.

[0157] According to one embodiment, a computer-implemented method includes: - obtaining, for the optical identifier reader of said at least one device 20PRE-1 included in the analysis system 20, a capability definition 32 characterizing at least one aspect of the optical identifier reader of said at least one device 20PRE-1 to at least one visual identifier 3A; further comprising Classifying the visual identifier 3A includes comparing the capability definition 32 of the optical identifier reader of the at least one device 20PRE-1 with a digital representation of the visual identifier 3A associated with the sample container 1. For example, if the visual identifier is a barcode, the capability definition 32 may define that the optical identifier reader of at least one device included in the analytical system is capable of reading barcodes up to one of a number of industry standard grades, such as up to Grade 0, Grade 1, Grade 2, Grade 3, or Grade 4 of the IS015416 standard.

[0158] For example, if the visual identifier is a QR code, the capability definition 32 may define that an optical identifier reader of at least one device included in the analytical system is capable of reading QR codes up to one of a number of industry standard grades, such as up to Grade 0, Grade 1, Grade 2, Grade 3, or Grade 4 of the ISO / IEC 18004:2015 standard.

[0159] Those skilled in the art will appreciate that the capability definition 32 of each optical identifier reader of a device does not necessarily have to be defined in accordance with the aforementioned standard, but rather can be characterized using a wide range of parameters accessible to or measured by an optical identifier reader, such as a barcode verifier or a QR code verifier.

[0160] According to one embodiment, identifying at least one device 20PRE-1 included in the analysis system 20 comprises: - obtaining order data including a first identifier of at least one analytical test to be performed by the analytical system 20 using the sample container 1 and / or at least one transfer action of the sample container 1 within the analytical system 20; The order data optionally includes a second identifier for identifying the particular analytical system 20 that will be used to perform the at least one analytical test.

[0161] Analytical tests ordered by a healthcare professional at the sample receiving facility 10 may be decomposed into workflows in a pre-processing step 71 performed by the data processing agent 30SERV prior to the execution of the test by the analytical system 20. Different analytical tests require sample containers to be processed according to different procedures by different analytical devices. According to a first option, visual identifier compatibility can be performed with reference to a generic analyzer system, based solely on manufacturer information regarding the capabilities of the optical identifier readers D1-D5. By associating the identifier of a specific analytical system 20 with the test order, calibration information of the optical identifier reader associated with the specific analyzer device can be incorporated into the decision whether a sample container with a measured visual identifier can be accepted by the analytical system 20.

[0162] According to one embodiment, the method further comprises, when the sample container 1 and / or the visual identifier 3A associated with the sample container 1 is classified with a negative classification result of the at least one device 20PRE-1, outputting a message defining that the visual identifier 3A associated with the sample container 1 has received a negative classification with respect to the at least one device 20PRE-1.

[0163] According to one embodiment, if the sample container 1 receives a negative classification, the computer of the sample receiving facility 10 may prevent the user from sending the sample container 1 to the analytical system 20. According to one embodiment, the user may receive a message via a graphical user interface of the computer of the sample receiving facility 10 providing information about defective parameters of the visual identifier to simplify troubleshooting. According to one embodiment, if the sample container 1 receives a negative classification, the computer of the sample receiving facility 10 may allow the user to send the sample container 1 to the analytical system 20, while simultaneously sending a message to the laboratory information system of the analytical system 20 advising a staff member at the facility hosting the analytical system 20 that the sample container 1 needs to be re-labeled before being accepted into the analytical system 20.

[0164] According to one embodiment, the method further comprises outputting to a laboratory information system associated with the at least one analytical system 20 a message including an identification code of the sample container 1 having the visual identifier 3A that has received a negative classification with respect to the at least one device 20PRE-1 included in the at least one analytical system 20 when the sample container 1 and / or the visual identifier 3A associated with the sample container 1 is classified with a negative classification result of the at least one device 20PRE-1.

[0165] According to one embodiment, if a message indicating a negative classification is output, the system is configured to prevent the sample vessel from proceeding to at least one analytical system.

[0166] According to one embodiment, if a message indicating a negative classification is output, the sample container is sent to a re-labeling device.

[0167] According to one embodiment, the at least one analytical system 20 comprises a set of devices used to perform at least one analytical test in a predetermined sequence defined by the analytical test identified by a first identifier in the order data 34, and the method comprises: comparing a plurality of capability definitions 32 corresponding to each device 20PRE-1 of the set of devices defined by the order data 34 with the digital representation of the visual identifier 3A; If the visual identifier 3A associated with the sample container 1 does not meet the capability definition 32 of at least one device 20PRE-1 included in the set of devices, outputting a message defining that the visual identifier 3A associated with the sample container 1 has received a negative classification for at least one device 20PRE-1 included in the set of devices, the message optionally identifying each device 20PRE-1 included in the set of devices that caused the negative classification; Further includes:

[0168] According to one embodiment, at least one analytical system 20 identified by a second identifier comprises a set of devices used to perform at least one analytical test in a predetermined sequence defined by the analytical test identified by the first identifier, and the method comprises: comparing a plurality of capability definitions 32, each corresponding to each device 20PRE-1 of the set of devices, with the digital representation of the visual identifier 3A; If the visual identifier 3A associated with the sample container 1 does not meet the optical capability definition 32 of at least one device 20PRE-1 included in the set of devices, - identifying, in at least one analysis system 20 identified by a second identifier, an alternative device 20PRE-2 having an optical capability definition 32 that enables decoding of the visual identifier 3A by the alternative device 20PRE-2; - using a replacement device 20PRE-2 of the at least one analytical system 20 to perform at least one analytical test performed using the sample container 1 defined by the first identifier; Further includes:

[0169] In this case, if it is predicted in advance that the device intended to be used to perform the workflow step of the analytical test cannot reliably read the visual identifier, the data processing agent 30SERV and / or the laboratory information system LIS will search for an alternative device in the analytical system 20 that can reliably read the visual identifier, and the workflow of the selected analytical test defined in the order data 34 will therefore be updated with the alternative device before the workflow is executed.

[0170] FIG. 7 shows a schematic diagram of a further example of a rule-based approach for inspection of sample containers.

[0171] According to one embodiment, comparing the capability definition 32 of the at least one device with the digital representation of the visual identifier 3A comprises: obtaining, for at least one device included in at least one analytical system 20, a rule set defining at least one rule of a capability definition 32 for the at least one device that must be satisfied by a visual identifier 3A associated with a sample container 1; and indicating that the visual identifier 3A associated with the sample container 1 is compatible with at least one device included in at least one analytical system 20 if the visual identifier 3A satisfies all or a predetermined subset of the rules of the rule set; Further includes:

[0172] If the visual identifier 3A associated with the sample container 1 does not satisfy all or a predetermined subset of the rules of the rule set, it indicates that the visual identifier 3A is not compatible with at least one device included in at least one analytical system 20.

[0173] According to one example, the rule set may include one or any combination of a barcode edge determination metric, a barcode minimum reflectance metric, a barcode minimum edge contrast metric, a barcode symbol contrast metric, a barcode modulation grade, a barcode defect grade, and / or a barcode decodability grade.

[0174] Figure 7 shows that for each sample container 1 associated with a visual identifier 3A entering the analyzer system 20, a visual identifier quality detection step 22 is performed and a visual identifier quality metric is extracted in step 23. A rules data set, such as that shown in data structure 32 in Figure 6, provides a set of rules for each device in the analysis system 20 such that the captured visual identifier is sufficiently decodable by the corresponding device. A test order 34 for each sample maps the destination of each sample to one or more analyzers in the analysis system 20. A rule checker 19 determines whether the quality of the visual identifier is compatible with the intended test order based on the rules for the analyzers included in the particular analyzer system 20. If so, the sample container is forwarded to downstream analyzers 20ANA-1, 20ANA-2 with sufficient certainty that the optical identifier readers D1, D2 associated with these analyzers can read the visual identifier 3A. If the rule tracker determines that the quality of the visual identifier 3A is not compatible with the optical identifier readers D1, D2 of the downstream analyzers 20ANA-1, 20ANA-2, the sample container 1 in question is separated from the set of sample containers and provided to a laboratory for replacement of the visual identifier.

[0175] FIG. 8 shows a schematic diagram of an example of a graphical user interface for inspecting a sample container.

[0176] In an exemplary setup, a specific test order and lab analyzer configuration table are provided that defines the analyzer to be used, e.g., in the form of a matrix. The combination of the analyzer to be used and the specific barcode reader capability information of the analyzer to be used defines the required barcode quality, e.g., in the form of a matrix. In addition to the required barcode quality, the specific barcode quality measured by the device to be used at the collection location defines whether it is OK or not OK for lab processing. Accordingly, an error message or OK is sent to the operator.

[0177] In the illustration of FIG. 8 , a smartphone 150 available at the sample reception facility 10 is used to photograph a sample container 1 bearing a visual identifier 3A. A first graphical user interface menu option, “Test Selection,” allows the user to select the diabetes symptom test “DIA” using menu 152. If the sample reception facility 10 has commercial arrangements with more than one laboratory, the specific laboratory intended to perform the test is selected using menu 153. Of course, the range of laboratories available in menu 153 may be modified based on the type of test selected via menu 152. Graphical user interface option 154 prompts the user to upload an image of the identifier 3A and / or its attachment to the sample container 1. Alternatively, or in addition, an optical identifier reader, such as a barcode reader or QR code reader, may be accessible to the user device. Thus, graphical user interface option 154 may also prompt the user to obtain and upload a visual identifier determination using the optical identifier reader. The software presents the classification results to the user in graphical user interface option 155. The results are derived by carrying out the method according to the first aspect, and if it is determined that the visual identifier 3A is of too low quality to allow the sample container 1 to enter the analysis system 20, the graphical user interface may provide the option to reprint the visual identifier 3A and may additionally or alternatively provide advice as to why the previous visual identifier failed to pass the determination according to the first aspect.

[0178] According to one embodiment, if the visual identifier associated with the sample container 1 can be decoded by at least one device of the analytical system 20, a message is output confirming that the visual identifier associated with the sample container 1 has received a positive classification with respect to at least one device of the at least one analytical system 20.

[0179] According to one embodiment, if at least one device 20PRE-1 of the analytical system 20 is unable to decode the visual identifier 3A associated with the sample container 1, - a user advice is generated based on a comparison of the digital representation of the visual identifier 3A with the capability definition 32 of the at least one device; The user advice is displayed by the user interface 151 of the user device 150.

[0180] According to one embodiment, if at least one device 20PRE-1 of the analytical system 20 is unable to decode the visual identifier 3A associated with the sample container 1, A further label is printed, comprising a visual identifier 3A associated with the sample container 1, using the label printer 10Print, 20Print.

[0181] According to one embodiment, the method includes storing a plurality of digital representations of visual identifiers 3A associated with a corresponding plurality of sample vessels; For each stored digital representation of a visual identifier 3A associated with a respective sample container: - modeling the substitution of at least one device included in at least one analytical system 20 with a second identifier, which is used to perform at least one analytical test with a first identifier; and the alternative device has a predetermined optical power definition 32.

[0182] 6 , after a comparison step 73 between a device included in the analytical system 20 and a digital representation of the visual identifier obtained at the sample receiving facility 10, it can be assumed that at least one device in the analytical system 20 reliably identifies the sample container bearing the visual identifier obtained at the sample receiving facility 10. According to this embodiment, the comparison step 73 identifies at least one alternative device in the analytical system 20 that can substitute for the workflow step defined by the order data 34. The comparison step 73 models the substitution of at least one alternative device in the workflow step defined by the order data 34 such that the test defined by the order data 34 can still be performed by the analytical system 20 without requiring an exchange of the visual identifier at the sample receiving facility 10. This approach minimizes the amount of label reprinting that needs to be performed at the sample receiving facility 10 while still allowing reliable processing of sample containers with associated visual identifiers.

[0183] According to a second aspect, there is provided a system 60 comprising an apparatus comprising an optical identifier reader 12, 22 and / or a camera 14, 24 configured to acquire a digital representation of a visual identifier 3A associated with a sample container 1. The system further comprises an analytical system 20 comprising at least one apparatus 20PRE-1 configured to perform at least one analytical test, and a communications network 62. The system further comprises a data processing agent 30 communicatively connected to the apparatus and the analytical system 20 via the communications network.

[0184] The data processing agent 30 is configured to obtain a digital representation of the visual identifier 3A associated with the sample container 1 and to identify at least one device included in the analytical system 20, the analytical system 20 intended to perform at least one analytical test using the sample container 1.

[0185] At least one device 20PRE-1 comprises an optical identifier reader, and the data processing agent is further configured to characterize the ability of the optical identifier reader of the at least one device included in the analysis system 20 to read the visual identifier 3A associated with the sample container 1, thereby classifying the visual identifier 3A associated with the sample container 1, generate a corresponding classification result characterizing the visual identifier 3A associated with the sample container 1, and output a message defining the classification result.

[0186] According to one embodiment, the system further comprises a label printer 10Print, 20Print. The data processing agent 30 is configured to send specifications to the label printer 10Print, 20Print based on the order data 34. The label printer 10Print, 20Print is configured to print replacement labels to be attached to the sample containers 1 according to the specifications received from the data processing agent.

[0187] According to a fourth aspect, there is provided a computer program element comprising machine-readable instructions which, when executed, perform a computer-implemented method according to the first aspect.

[0188] According to a fifth aspect, there is provided a computer readable medium encoding a computer program element according to the fourth aspect.

[0189] FIG. 9 shows a schematic of an optional example of classification of sample containers by machine learning.

[0190] According to a sixth aspect, there is provided a computer-implemented method 80 for training a classifier of analytical sample containers comprising visual identifiers 3A, the method comprising: obtaining a training set comprising a plurality of digital representations of visual identifiers 3A associated with a corresponding plurality of sample containers; - labeling each digital representation in the training set with a first identifier of at least one analytical test to be performed using the sample container 1 and a second identifier of at least one analytical system 20 used to perform the at least one analytical test; - obtaining a result set defining, for each digital representation in the training set, a determination of whether the corresponding visual identifier 3A was correctly read by all devices of the at least one analysis system 20; using a machine learning process to train a classifier using the training set and the corresponding result set; Includes:

[0191] According to one embodiment, a computer-implemented method 80 classifies a visual identifier associated with a sample container 1 based on a comparison between a capability definition 32 and a digital representation of the visual identifier, performed at least in part using a classifier trained according to the sixth aspect.

[0192] Thus, in addition to providing a static (one universal threshold) look-up table of barcode compatibility based on a single measurement of an optical identifier reader within the device, another implementation is optionally based on continuous feedback and learning of the capabilities of the optical indicator identifiers included in the devices within the analytical system 20. According to one embodiment, a machine learning algorithm is applied, which is trained on a dataset comprising digital representations of a plurality of sample containers, as well as the success rate of the optical indicator identifiers within the analytical system 20 in successfully classifying the visual identifiers associated with the sample containers. In particular, this allows tracking the impact or drift of the optical identifier reader's performance over time as environmental or aging effects change the optical identifier reader's ability to read the visual identifiers.

[0193] 9, a training set TS containing various visual identifiers, such as barcodes or QR codes, is typically used to train a machine learning model M by passing several calibration sample containers containing calibration identifiers through the analytical system 20 and recording, for each pair of calibration sample container and device of the analytical system 20, whether the given sample container can be correctly decoded by the corresponding device of the analytical system. The machine learning model M is trained on the data pairs so obtained. The machine learning model M may, for example, replace and / or supplement a rules data set stored in the data store 32.

[0194] According to a seventh aspect, there is provided a computer program element comprising machine-readable instructions which, when executed, performs a computer-implemented method according to the fifth aspect.

[0195] According to an eighth aspect, there is provided a computer readable medium encoding a computer program element according to the seventh aspect.

[0196] According to a ninth aspect, there is provided a machine learning model comprising machine readable instructions to generate an output data vector in accordance with the classifier trained according to the sixth aspect when provided with an input data vector.

[0197] FIG. 10 shows a schematic diagram of an example of an apparatus according to the third embodiment.

[0198] According to a third aspect, an apparatus 90 is provided that comprises a communications interface 96, a processor 92, and a memory interface 94.

[0199] The processor is configured to host a data processing agent that, in use, is communicatively connected via a communications network to the device and to the analytical system 20. The data processing agent is configured to obtain a digital representation of a visual identifier 3A associated with the sample container 1 and to identify at least one device included in the analytical system 20, the analytical system 20 intended to perform at least one analytical test using the sample container 1.

[0200] The data processing agent is further configured to classify the visual identifier 3A associated with the sample container 1 by characterizing the ability of the optical identifier reader of at least one device included in the analysis system 20 to read the visual identifier 3A associated with the sample container 1, thereby generating a corresponding classification result characterizing the visual identifier 3A associated with the sample container 1, and output a message defining the classification result.

[0201] A computer program is also disclosed, which includes computer-executable instructions for performing the method according to the present disclosure in one or more of the embodiments contained herein when the program is executed on a computer or a computer network. Specifically, the computer program may be stored in a computer-readable data carrier. In another example, the computer program may be a cloud-based computer program. Thus, specifically, one, several, or all of the method steps disclosed herein may be performed by using a computer or a computer network, preferably by using a computer program.

[0202] A computer program product is further disclosed, having a program code for performing the method according to the present disclosure in one or more of the embodiments contained herein when the program is run on a computer or a computer network. In particular, the program code may be stored on a computer-readable data carrier.

[0203] A data carrier is further disclosed for storing a data structure, such as a working memory or main memory of a computer or computer network, that, after being loaded into a computer or computer network, is capable of performing a method according to one or more of the embodiments disclosed herein.

[0204] A computer program product having program code stored on a machine-readable carrier is also disclosed, which, when run on a computer or computer network, performs the methods according to one or more of the embodiments disclosed herein. In particular, the computer program product may be distributed over a data network.

[0205] Further disclosed is a modulated data signal comprising instructions readable by a computer system or computer network for performing a method according to one or more of the embodiments disclosed herein.

[0206] Further disclosed is a computer loadable data structure configured to perform a method according to one of the embodiments described herein when executed on a computer.

[0207] The features disclosed in the foregoing description, or the appended claims, or in the drawings, whether described as apparatus features, means for performing a disclosed function, or method or process steps, may be used separately or in any combination to provide the present invention. While the present invention has been described in terms of embodiments herein, those skilled in the art will be able to derive numerous equivalent modifications and variations based on this disclosure. Accordingly, the exemplary embodiments disclosed above are for purposes of illustration and not limitation. Modifications to the embodiments described herein may be provided without altering the spirit and scope of the invention. The paragraph headings introduced herein are not intended to limit the described subject matter. In this specification and the appended claims, the terms "comprise" and "include" and variations thereof should be understood to mean the inclusion of the described feature, step, or group of features, but not the exclusion of other features.

[0208] statement The present invention is also defined according to the following embodiments.

[0209] A. A computer-implemented method of an apparatus for sorting analytical sample containers, comprising: obtaining a digital representation of a visual identifier associated with the sample container; - obtaining order data including a first identifier of at least one analytical test or transfer action to be performed by an analytical system using the sample container, and optionally including a second identifier of at least one analytical system used to perform the at least one analytical test; transmitting a digital representation of the visual identifier to a data processing agent via a communications network; - transmitting order data including the first identifier and optionally including the second identifier to a data processing agent via a communications network; receiving, via a communications network, a message from the data processing agent defining a classification result that defines a decision metric characterizing the ability of an optical identifier reader of at least one device included in the analytical system to decode a visual identifier associated with a sample container; -Output a message that defines the classification result 11. A computer-implemented method comprising:

[0210] B. A computer-implemented method of a data processing agent for classification of analytical sample containers, comprising: receiving a digital representation of the visual identifier to a data processing agent via a communication network connected to the device for classification of analytical sample containers; - receiving order data from the device to the data processing agent, the order data including the first identifier and, optionally, the second identifier; - identifying at least one device included in an analytical system for performing at least one analytical test using the sample container, the device having an optical identifier reader; - classifying the sample containers associated with the visual identifiers based on a decoding metric characterizing the ability of an optical identifier reader of at least one device included in the analytical system to decode the visual identifier associated with the sample container, thereby generating a corresponding classification result characterizing the sample containers associated with the visual identifiers; - sending a message defining the classification result to a device for classification of analytical sample containers; 11. A computer-implemented method comprising:

[0211] C. An apparatus for sorting analytical sample containers, comprising: a communication interface; a processor; an optical identifier reader; -User interface and Equipped with the optical identifier reader is configured to obtain a digital representation of the sample vessel having the associated visual identifier; The processor is configured to send a digital representation of the visual identifier to a data processing agent via a communication interface, the processor is configured to receive from the data processing agent a message defining a decoding metric characterizing the ability of an optical identifier reader of at least one device included in the analysis system to decode the visual identifier associated with the sample container, and the user interface is configured to output a message defining the classification result.

[0212] D. A data processing agent for classification of analytical sample containers, a communication interface; -Processor and Equipped with the communication interface is configured to receive a digital representation of the visual identifier from the apparatus for classification of analytical sample containers to the data processing agent; A data processing agent, wherein the processor is configured to identify at least one device included in an analytical system intended to perform at least one analytical test using a sample container, the at least one device comprising an optical identifier reader, the processor is configured to classify a visual identifier associated with the sample container based on a decoding metric characterizing the ability of the optical identifier reader of the at least one device included in the analytical system to decode the visual identifier associated with the sample container, the processor is further configured to generate a corresponding classification result characterizing the sample container with which the visual identifier is associated, and the processor is further configured to send a message via the communication interface defining the classification result to the device for classification of the analytical sample container.

[0213] E. A computer-implemented method for sorting analytical sample containers, comprising: obtaining a digital representation of a visual identifier associated with the sample container; - identifying, using the first and second identifiers, at least one device included in at least one analytical system used to perform at least one analytical test; - obtaining order data including a first identifier of at least one analytical test to be performed using the sample container, and optionally further including a second identifier of at least one analytical system to be used to perform the at least one analytical test; - obtaining an optical capability definition of an optical identifier reader of at least one device; comparing the optical capability definition of at least one device with a digital representation of the visual identifier; classifying the sample container associated with the visual identifier based on a comparison between the optical capability definition and the digital representation of the visual identifier; 11. A computer-implemented method comprising:

Claims

1. 1. A computer-implemented method (80) for sorting analytical sample containers, comprising: Obtaining (82) a digital representation of a visual identifier (3A) associated with the sample container (1); Identifying (84) at least one device (20PRE-1) included in an analytical system (20) intended to perform at least one analytical test using said sample container (1), said device (20PRE-1) being equipped with an optical identifier reader; characterizing the ability of the optical identifier reader and / or camera of the at least one device (20PRE-1) included in the analysis system (20) to decode the visual identifier (3A) associated with the sample container (1), thereby classifying the sample container (1) with which the visual identifier (3A) is associated, thereby generating a corresponding classification result (86) characterizing the sample container (1) with which the visual identifier (3A) is associated; outputting a message defining the classification result (88); A computer-implemented method (80) comprising:

2. Obtaining a capability definition (32) for the optical identifier reader and / or camera of the at least one device (20PRE-1) included in the analysis system (20) that characterizes at least one aspect of the optical identifier reader of the at least one device (20PRE-1) for decoding at least one visual identifier (3A). further comprising 2. The computer-implemented method (80) of claim 1, wherein classifying the visual identifier (3A) includes comparing the capability definition (32) of the optical identifier reader of the at least one device (20PRE-1) with the digital representation of the visual identifier (3A) associated with the sample container (1).

3. Identifying the at least one device (20PRE-1) included in the analysis system (20) comprises:

3. The computer-implemented method (80) of claim 1 or 2, further comprising obtaining order data including a first identifier of at least one analytical test to be performed by an analytical system (20) using the sample container (1) and / or at least one transfer action of the sample container (1) within the analytical system (20), the order data optionally including a second identifier for identifying the specific analytical system (20) used to perform the at least one analytical test.

4. If the sample container (1) and / or the visual identifier (3A) associated with the sample container (1) is classified with a negative classification result of the at least one device (20PRE-1), outputting a message defining that the visual identifier (3A) associated with the sample container (1) has received a negative classification with respect to the at least one device (20PRE-1); and / or outputting a message to a laboratory information system associated with said at least one analytical system (20) containing an identification code of the sample container (1) having said visual identifier (3A) that has been negatively classified with respect to said at least one device (20PRE-1) included in said at least one analytical system (20); The computer-implemented method (80) of any one of claims 1 to 3, further comprising:

5. the at least one analytical system (20) comprises a set of devices used to perform the at least one analytical test in a predetermined sequence defined by the analytical test identified by the first identifier in the order data (34); comparing a plurality of capability definitions (32) corresponding to each device (20PRE-1) of the set of devices defined by the order data (34) with the digital representation of the visual identifier (3A); If the visual identifier (3A) associated with the sample container (1) does not meet the capability definition (32) of at least one device (20PRE-1) included in the set of devices, outputting a message defining that the visual identifier (3A) associated with the sample container (1) has received a negative classification with respect to the at least one device (20PRE-1) in the suite of devices, the message optionally identifying each device (20PRE-1) in the suite of devices that caused the negative classification; The computer-implemented method (80) of claim 3 or 4, further comprising:

6. the at least one analytical system (20) identified by the second identifier comprises a set of devices used to perform the at least one analytical test in a predetermined sequence defined by the analytical test identified by the first identifier; comparing a plurality of capability definitions (32) corresponding to each device (20PRE-1) of the suite of devices with the digital representation of the visual identifier (3A); If the visual identifier (3A) associated with the sample container (1) does not meet the optical capability definition (32) of at least one device (20PRE-1) included in the set of devices, In the at least one analytical system (20) identified by the second identifier, identifying an alternative device (20PRE-2) having an optical capability definition (32) that enables the alternative device (20PRE-2) to read the visual identifier (3A); and using the replacement device (20PRE-2) of the at least one analytical system (20) to perform the at least one analytical test performed using the sample container (1) defined by the first identifier; The computer-implemented method (80) of any one of claims 3 to 5, further comprising:

7. If the at least one device (20PRE-1) of the analytical system (20) is unable to decode the visual identifier (3A) associated with the sample container (1), generating a user advice based on a comparison of the capability definition (32) of the at least one device with the digital representation of the visual identifier (3A); and Displaying said user advice by a user interface (151) of a user device (150), and / or printing a further label containing said visual identifier (3A) associated with said sample container (1) using a label printer (10Print, 20Print); The computer-implemented method (80) of any one of claims 1 to 6, further comprising:

8. Comparing the capability definition (32) of the at least one device with the digital representation of the visual identifier (3A) comprises: obtaining, for the at least one device included in the at least one analytical system (20), a rule set defining at least one rule of the capability definition (32) for the at least one device that must be satisfied by a visual identifier (3A) associated with a sample container (1); and Indicating that the visual identifier (3A) associated with a sample container (1) is compatible with the at least one device included in the at least one analytical system (20) if the visual identifier (3A) satisfies all or a predetermined subset of the rules of the rule set, or indicating that the visual identifier (3A) is not compatible with the at least one device included in the at least one analytical system (20) if the visual identifier (3A) associated with a sample container (1) does not satisfy all or a predetermined subset of the rules of the rule set. The computer-implemented method (80) of any one of claims 2 to 7, further comprising:

9. The capability definition (32) of the optical identifier reader of the at least one device characterizes at least one or any combination of: a range of acceptable misalignment of the visual identifier with respect to a longitudinal axis of a sample container; a range of acceptable occlusion of the visual identifier; a range of acceptable vertical and / or horizontal dimensions of the visual identifier; a range of acceptable blur or resolution artifacts of the visual identifier; a range of acceptable contrast or brightness ratios of the visual identifier; and / or a range of acceptable artifacts of the visual identifier; 9. The computer-implemented method of claim 2, wherein the capability definition of the optical identifier reader of the at least one device further characterizes at least one or any combination of a barcode edge determination metric, a barcode minimum reflectance metric, a barcode minimum edge contrast metric, a barcode symbol contrast metric, a barcode modulation grade, a barcode defect grade, and / or a barcode readability grade.

10. storing a plurality of digital representations of visual identifiers (3A) associated with a corresponding plurality of sample vessels; For each stored digital representation of a visual identifier (3A) associated with each sample container: modeling the substitution of at least one device included in said at least one analytical system (20) by said second identifier, used to perform said at least one analytical test by said first identifier, said substitution device having a predetermined optical capability definition (32); and optionally outputting a corresponding identifier of one or more sample vessels provided with a visual identifier (3A) that is negatively classified; The computer-implemented method (80) of any one of claims 3 to 9, further comprising:

11. 1. A computer-implemented method (80) for training a classifier for analytical sample containers with visual identifiers (3A), comprising: obtaining a training set comprising a plurality of digital representations of visual identifiers (3A) associated with a corresponding plurality of sample containers; labeling each digital representation in the training set with a first identifier of at least one analytical test to be performed using the sample container (1) and a second identifier of at least one analytical system (20) used to perform the at least one analytical test; obtaining a result set defining, for each digital representation in the training set, a determination of whether the corresponding visual identifier (3A) was correctly read by all devices of the at least one analytical system (20); training a classifier using said training set and said corresponding result set using a machine learning process; A computer-implemented method (80) comprising:

12. an apparatus comprising an optical identifier reader (12, 22) and / or a camera (14, 24) configured to acquire a digital representation of a visual identifier (3A) associated with a sample container (1); an analytical system (20) comprising at least one device (20PRE-1) configured to perform at least one analytical test; a communications network (62); a data processing agent (30) communicatively connected to the device and the analysis system (20) via the communication network; Equipped with the data processing agent (30) is configured to obtain a digital representation of a visual identifier (3A) associated with the sample container (1) and to identify at least one device included in an analytical system (20), the analytical system (20) being intended to perform at least one analytical test using the sample container (1); The at least one device (20PRE-1) comprises an optical identifier reader, and the data processing agent is further configured to: classify the visual identifier (3A) associated with the sample container (1) by characterizing the ability of the optical identifier reader of the at least one device included in the analysis system (20) to decode the visual identifier (3A) associated with the sample container (1), thereby generating a corresponding classification result characterizing the visual identifier (3A) associated with the sample container (1), and output a message defining the classification result.

13. Label printer (10 Print, 20 Print) Furthermore, The data processing agent (30) is configured to send specifications to the label printer (10Print, 20Print) based on the order data (34); 13. The system (60) of claim 12, wherein the label printer (10Print, 20Print) is configured to print replacement labels to be attached to sample containers (1) according to the specifications received from the data processing agent.

14. A computer program element comprising machine-readable instructions that, when executed, perform the computer-implemented method of any one of claims 1 to 10 or claim 11.

15. a communication interface (96); a processor (92); A memory interface (94) Equipped with the processor is configured to host a data processing agent that, in use, is communicatively connected to the device and analysis system (20) via a communications network; the data processing agent is configured to obtain a digital representation of a visual identifier (3A) associated with the sample container (1) and to identify at least one device included in the analytical system (20), the analytical system (20) being intended to perform at least one analytical test using the sample container (1); The data processing agent is further configured to classify the visual identifier (3A) associated with the sample container (1) by characterizing the ability of an optical identifier reader of the at least one device included in the analysis system (20) to decode the visual identifier (3A) associated with the sample container (1), thereby generating a corresponding classification result characterizing the visual identifier (3A) associated with the sample container (1), and output a message defining the classification result.

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