Sample Tube Tag Readability Classification for Lab Routing
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Solution Overview
Problem
Automated laboratory systems face inefficiencies due to deteriorated identification tags on sample tubes, such as barcodes, which can lead to incorrect handling or processing, as existing methods rely on reference levels rather than real-time reading performance of tag readers.
Innovation Solution
A method and system for classifying identification tags on sample tubes using a classifying device that predicts tag readability by each laboratory device's reader, allowing for optimized handling and processing by determining whether the tag information can be recognized, and controlling the workflow based on actual reader performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sample tubes with deteriorated identification tags are processed in the automated laboratory system, then the system can handle more samples, but the tag reading accuracy deteriorates leading to incorrect handling or processing
Solution Approach 1:
The system performs preliminary classification of identification tags using a classifying device with image analysis before samples enter the main processing workflow. This advance assessment allows the system to identify and route samples with deteriorated tags to appropriate handling procedures, preventing reading errors during normal processing while maintaining high throughput.
Solution Approach 2:
A dedicated classifying device acts as an intermediary between sample input and the main automated laboratory system. This intermediary component performs preliminary tag assessment and provides classification results to the control unit, enabling the system to make informed routing decisions without bottlenecking the main processing workflow.
2Adaptability or versatility
If the system routes all samples to all laboratory devices, then device utilization is maximized, but time is lost due to rework when tags cannot be read
Solution Approach 1:
The control unit receives feedback from the classifying device regarding tag condition and uses this information to make intelligent routing decisions. This feedback mechanism allows the system to adapt sample routing dynamically, directing samples to appropriate devices based on actual tag readability prospects rather than following fixed routing rules, thereby avoiding unnecessary rework cycles.
Solution Approach 2:
The sample routing system transitions from static, predetermined routing to dynamic, condition-based routing. The control unit adjusts routing decisions in real-time based on classifying device assessments of tag condition, allowing the system to optimize both device utilization and processing time by adapting to actual sample characteristics.
3Device complexity
If a reference level for tag quality is used, then the classification process is simple, but it does not reflect the real-time reading performance of actual tag readers
Solution Approach 1:
The system transitions from using a fixed reference quality level to evaluating multiple actual reading results from the tag reader. By changing the parameter basis from a single reference threshold to multiple empirical reading outcomes, the system achieves more reliable predictions of actual tag readability while maintaining computational feasibility through the classifying device's image analysis capabilities.
Data Source
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AI summary
The present disclosure refer to a method for classifying an identification tag on a sample tube containing a sample to be processed in an automated laboratory system having a plurality of laboratory devices (4), the method comprising providing a sample tube (5) having an identification tag (9) and containing a sample (6) to be analyzed . Each of the plurality of laboratory devices (4) is assigned a tag reader device (10) configured to read the identification tag (9) for recognizing identification tag information. The method further comprises, in a classifying device (2): providing a classifying module (8) configured to predict whether the identification tag information can be recognized from measured tag data detected by the tag reader device (10) assigned to the laboratory device and being indicative of characteristics of the identification tag (9); reading the identification tag (9) on the sample tube (5) by a classifying reader device (7), thereby, providing the measured tag data for the identification tag (9) indicative of tag characteristics of the identification tag (9) on the sample tube (5); determining the tag characteristics for the identification tag (9) from the measured tag data; in the classifying module (8), receiving the tag characteristics, and predicting whether the tag reader device (10) of at least one of the plurality of laboratory devices (4) can read the identification tag (9); and providing classification data being (i) first classification data indicative of predicting, by the classifying module (8), the identification tag (9) being readable by the tag reader device (10) of at least one of the plurality of laboratory devices (4); and (ii) second classification data which are different from the first classification data and indicative of predicting, by the classifying module (8), the identification tag (9) being not readable by the tag reader device (10) of at least one of the plurality of laboratory devices (4). Further, an automated laboratory system (1) for processing a sample tube containing a sample for at least one of pre-analytics and sample analysis is provided.