Sample Tube Rack Tracking With Computer Vision for Automated Transfer
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Solution Overview
Problem
The traditional biological sample collection and testing process is prone to errors due to manual handling and lack of digital organization, requiring significant manpower and leading to inefficiencies, especially in high-volume sample processing.
Innovation Solution
A system utilizing computer vision and machine learning to track and manage biological samples through QR codes and barcodes, enabling automated sample registration, real-time status updates, and secure data management with machine learning models for PCR test analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual handling and organization of samples is used, then flexibility in sample processing is maintained, but error rate increases and processing speed decreases
Solution Approach 1:
The patent replaces manual mechanical handling of samples with an automated vision-based system. Cameras capture images of sample tubes and racks, computer vision algorithms automatically identify and track samples, eliminating manual scanning and data entry operations. This substitution directly resolves the contradiction by providing both high accuracy (through automated identification) and high speed (through parallel image processing).
Solution Approach 2:
The system creates digital copies of sample information through image capture and processing. Instead of manually transcribing sample data from physical labels to digital systems, the vision system directly captures label information and creates digital representations automatically. This copying mechanism eliminates transcription errors and accelerates the digitization process, simultaneously improving reliability and productivity.
2Reliability
If manual entry of samples into the system is performed, then system flexibility is maintained, but time consumption increases and error probability increases
Solution Approach 1:
The patent replaces manual data entry operations with automated computer vision-based identification. The system captures images of sample labels and rack positions, processes these images through algorithms that automatically extract and input data into the laboratory information management system. This eliminates manual typing and form filling, dramatically reducing both time consumption and error rates in sample accessioning.
Solution Approach 2:
The system enables samples to effectively self-register through the vision system. When samples are placed in racks, the automated system automatically detects their presence, reads their identifiers, determines their positions, and inputs all necessary data without human intervention. This self-service mechanism transforms sample accessioning from a labor-intensive manual process to an automated self-processing operation.
3Productivity
If digital organization of samples is implemented, then processing efficiency increases, but system complexity increases
Solution Approach 1:
The patent implements a multi-functional vision-based platform that handles multiple sample management tasks through a single integrated system. The same camera system and software platform perform sample identification, rack position tracking, label reading, and data input operations. This universal system replaces multiple separate manual processes (visual inspection, manual scanning, data entry), reducing operational complexity despite the advanced technology employed.
Solution Approach 2:
The system introduces a software-based intermediary layer between the physical sample handling and the laboratory information management system. This software intermediary automatically captures image data, processes it through vision algorithms, and translates it into structured data for the LIMS. This intermediary simplifies the overall system architecture by providing a standardized interface that handles the complexity of image processing and data extraction internally.
Data Source
AI summary
Methods, systems and apparatus for the tracking and managing of biological samples. A provider may check-in or register a patient at a provider facility. A sample tube may be registered to the patient, and a biological sample may be collected. The provider aggregates a plurality of sample tubes into cells of a rack. The provider then captures an image of the rack. A computer vision operation may be performed to isolate and identify a QR code affixed to the lid of each sample tube. The identified sample tubes are registered to the rack in which they are held, and the rack transferred to a destination lab. The same computer vision operation may be performed upon receipt of the rack. The samples may then be transferred to a sample plate. The samples are transferred from a cell in the rack to a corresponding well in the sample plate.