ELISpot Image Analysis for Active vs Latent TB Diagnosis
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Solution Overview
Problem
Current methods for distinguishing active tuberculosis (ATB) from latent tuberculosis infection (LTBI) using the T-SPOT.TB test are unreliable due to challenges in accurately counting spots and interpreting spot count ratios, leading to a high margin of error.
Innovation Solution
A trained machine learning algorithm model processes image data from an ELISpot assay to determine the disease status of an individual, eliminating the need for manual spot counting and providing more accurate differentiation between ATB and LTBI.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual spot counting methods are used to interpret T-SPOT.TB test results, then the diagnostic process can be performed with simple equipment, but the measurement precision and reliability of tuberculosis diagnosis deteriorates due to high margin of error in spot count ratios
Solution Approach 1:
The patent replaces manual mechanical spot counting with an automated image processing system using machine learning algorithms. The system captures images of ELISpot assay wells and uses trained neural networks to automatically identify, count, and interpret spots, eliminating human subjectivity and error while maintaining operational simplicity through automated analysis pipelines
Solution Approach 2:
The patent introduces an intermediary image processing layer between the ELISpot assay and final diagnosis. This intermediary system captures visual data of spots and processes it through machine learning models, serving as a bridge that translates raw visual information into reliable diagnostic results without requiring direct manual counting
2Measurement precision
If automated ELISpot plate image readers and software are used to count spots, then the measurement precision improves, but the device complexity and cost increase
Solution Approach 1:
The patent develops a multi-functional machine learning system that can handle multiple aspects of diagnostic analysis: spot detection, spot counting, quality control assessment, and disease status differentiation. This universal system consolidates multiple specialized functions into a single platform, reducing the need for separate specialized equipment while maintaining high measurement precision
Solution Approach 2:
The machine learning model performs self-training and self-optimization through automated processing of training datasets. The system automatically adjusts its parameters and improves its accuracy through exposure to labeled examples, reducing the need for manual calibration and expert intervention while maintaining high counting precision
3Adaptability or versatility
If spot count ratios (TBAg:PHA) are used to differentiate active tuberculosis from latent infection, then the diagnostic method can distinguish between disease states, but the reliability deteriorates due to difficulty in accurately determining spot counts and high margin of error
Solution Approach 1:
The patent transforms the diagnostic approach by changing from manual spot count parameter extraction to automated image-based parameter measurement. The machine learning system extracts multiple parameters simultaneously (spot count, spot size, distribution patterns, intensity) and uses their relationships to differentiate disease status, improving reliability through more robust and precise parameter measurement
Solution Approach 2:
The system incorporates feedback loops where the machine learning model continuously refines its differentiation capability by comparing predicted disease status with known outcomes from training data. This feedback mechanism improves the reliability of disease status differentiation by constantly optimizing the relationship between spot pattern parameters and actual disease states
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method enhances the reliability of tuberculosis diagnosis by accurately characterizing the infection as active or latent, improving diagnostic precision and reducing errors associated with manual spot counting.
Implementation Method 1
processing input data using a trained machine learning algorithm model to generate an output representing information about the disease status of the individual
Implementation Method 2
The T-SPOT.TB test is an enzyme-linked immunospot (ELISpot) based interferon gamma release assay (IGRA)
Implementation Method 3
Following a colorimetric reaction, the INF-γ footprint of each responding cell can be visualised as a 'spot' and counted
Data Source
AI summary
The disclosure concerns a method of obtaining information about the disease status of an individual by processing input data using a trained machine learning algorithm model to generate an output representing the information. The disclosure also concerns a related computer program, data processing apparatus, and system, as well as a method for training a machine learning algorithm model to generate information about the disease status of an individual. The information may, for example, relate to the presence, absence or type of M. tuberculosis complex infection in the individual.