Diagnostic Tube Imaging for Accurate ML Tube Type Classification

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Solution Overview

Problem

Automated testing systems face variations in performance when characterizing sample tubes, leading to improper identification of tube types due to inconsistent manufacturer-dependent standards for cap colors and shapes, necessitating manual entry of information by lab technicians.

Innovation Solution

A method and apparatus that utilize machine learning models, trained using images of tube assemblies, to automatically classify and group tube types based on physical characteristics and cap colors, enabling real-time identification and reducing manual entry.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automated testing systems use digital imaging to characterize sample tubes, then productivity is improved by reducing manual entry, but measurement precision deteriorates due to variations in performance and improper characterization of tube types

Engineering Contradiction:

Improveautomation of tube characterizationVSAvoidaccuracy of tube type identification

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system performs preliminary imaging of tube assemblies at multiple positions (upright, inverted, and angled views) before final classification. This preliminary capture of comprehensive visual data enables the machine learning model to accurately characterize tube types, cap colors, and shapes even under varying lighting and positioning conditions, thereby maintaining measurement precision while achieving automation

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The machine learning model uses feedback from multiple image captures at different orientations to iteratively improve classification accuracy. The system compares images from various angles and positions, using the feedback from each comparison to refine its characterization of tube assemblies, thereby resolving the contradiction between automated processing and accurate identification

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If the system uses manufacturer-dependent standards for cap colors and shapes, then adaptability is improved for different tube types, but reliability deteriorates due to inconsistent standards leading to improper characterization

Engineering Contradiction:

Improvecapability to handle different tube typesVSAvoidconsistency of tube type identification

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The machine learning model is trained to recognize multiple manufacturer-specific tube assembly standards simultaneously. It learns universal features across different manufacturers' cap colors, shapes, and tube configurations, enabling the system to adaptively identify tube types from various manufacturers while maintaining consistent and reliable characterization through a unified classification framework

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system changes the parameters used for tube assembly characterization by analyzing multiple visual parameters (cap color, cap shape, tube diameter, tube length, positioning angles) rather than relying on a single manufacturer-specific standard. This multi-parameter approach enables the system to adapt to different manufacturers while maintaining reliable and consistent identification through comprehensive feature comparison

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If manual entry of tube information is required, then measurement precision is maintained through accurate data input, but productivity deteriorates due to increased manual effort

Engineering Contradiction:

Improveaccuracy of tube informationVSAvoidspeed of tube characterization

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system performs self-service by automatically capturing images of tube assemblies and using machine learning algorithms to extract and classify tube type information without human intervention. The model autonomously characterizes cap colors, shapes, and tube features, then groups assemblies by type, thereby maintaining accurate information extraction while dramatically improving productivity by eliminating manual data entry

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical process of manual data entry with an automated optical and computational system. Instead of technicians manually recording tube information, the system uses digital imaging devices to capture tube appearances and machine learning models to automatically extract and classify features, substituting human manual work with automated vision-based recognition that maintains precision while boosting productivity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12475681B2Apparatus and methods of training models of diagnostic analyzers

Publication Date: 2025.11.18 SIEMENS HEALTHCARE DIAGNOSTICS INC

AI summary

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