Intrinsic Feature Tracking for Rapid Microbial Detection in Liquids
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Solution Overview
Problem
Current diagnostic methods for bacterial infections, particularly urinary tract infections (UTIs), are slow and often require culture-based techniques, leading to empirical treatments and exacerbating antimicrobial resistance, with existing rapid methods being expensive, requiring extensive datasets, or having low sensitivity and specificity.
Innovation Solution
A method using intrinsic features of microorganisms, such as light scattering intensity and position profiles, to rapidly identify bacteria in urine samples within 10 minutes through large volume solution scattering imaging (LVSi) and machine learning, without the need for culturing, enabling point-of-care detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard culture-based diagnostic methods are used, then reliability of bacterial identification is improved, but loss of time increases (2-4 days for results)
Solution Approach 1:
The system performs preliminary actions by capturing multiple images of particles in the sample over time before analysis. The imaging system records particle positions and light scattering intensities across multiple time points, creating a temporal dataset that enables rapid identification without waiting for bacterial culture growth. This preliminary capture of temporal information allows the machine learning algorithm to distinguish pathogenic bacteria from non-pathogenic particles within minutes rather than days.
Solution Approach 2:
The invention replaces the mechanical/biological process of bacterial culturing with an optical detection system. Instead of relying on bacterial growth in culture media over 2-4 days, the system uses light scattering imaging to detect intrinsic optical properties of individual particles. The machine learning algorithm processes these optical signatures to identify pathogenic bacteria, substituting a physical-chemical measurement approach for the biological cultivation process.
2Loss of time
If rapid detection methods like dipstick tests and manual microscopy are used, then loss of time is reduced, but measurement precision decreases (lower reliability compared to culture-based methods)
Solution Approach 1:
The system adds temporal and multi-parameter dimensions to particle analysis. Instead of relying on single-point observations like dipstick tests or static microscopy images, the system captures particle positions, light scattering intensities, and motion trajectories across multiple time points. This multi-dimensional data (spatial position, temporal evolution, optical intensity variations) provides rich features for machine learning classification, achieving both rapid detection and high precision.
Solution Approach 2:
The invention introduces machine learning algorithms as an intermediary between raw optical measurements and bacterial identification. The machine learning model processes the complex multi-parameter temporal data from light scattering imaging, learning to distinguish pathogenic bacteria from non-pathogenic particles based on patterns in particle motion, size, shape, and optical properties. This intermediary enables automated, high-precision classification that surpasses manual microscopy while maintaining rapid throughput.
3Measurement precision
If extrinsic markers like fluorochromes and magnetic beads are used for particle analysis, then measurement precision is improved, but device complexity and loss of substance increase (additional sample processing required)
Solution Approach 1:
The system enables particles to serve themselves by detecting their intrinsic optical properties without requiring external markers. Each particle's natural light scattering behavior, size, shape, and motion characteristics provide sufficient information for identification. The machine learning algorithm learns to recognize pathogenic bacteria based on these inherent properties, eliminating the need for fluorochrome labeling, magnetic bead attachment, or other extrinsic marker systems that complicate sample processing.
Solution Approach 2:
The invention extracts and utilizes the intrinsic optical and physical properties of particles directly from the sample. By focusing on natural light scattering intensities, particle sizes, shapes, and motion trajectories, the system removes the need for additional labeling steps, reagents, or complex sample preparation procedures. This extraction of intrinsic features simplifies the overall diagnostic workflow while maintaining high identification accuracy.
4Ease of operation
If automated devices based on light scattering analysis are used, then ease of operation is improved, but measurement precision decreases (30-70% sensitivity rates compared to manual microscopy)
Solution Approach 1:
The invention introduces advanced machine learning algorithms as an intermediary between automated light scattering measurements and bacterial identification. The machine learning model processes complex temporal patterns in particle motion, size, shape, and optical intensity variations, learning to distinguish pathogenic bacteria from non-pathogenic particles with high accuracy. This intermediary enables automated devices to achieve sensitivity rates comparable to or exceeding manual microscopy while maintaining the advantages of automation and high throughput.
Solution Approach 2:
The system compensates for simplified automated measurement by adding temporal and multi-parameter dimensions to the analysis. Instead of relying on single static light scattering measurements, the system tracks particle positions, intensities, and motions across multiple time points, creating rich temporal datasets. This multi-dimensional approach provides sufficient information for high-precision automated identification, overcoming the limitations of simple automated light scattering devices.
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
Enables rapid, accurate differentiation of pathogenic microorganisms from other particles in clinical samples, reducing turnaround time to 10 minutes and conserving laboratory resources by eliminating the need for culture-based isolation.
Implementation Method 1
extracting two or more image features for each of a plurality of objects in a liquid sample from a set of imaging data that comprises multiple images of the liquid sample generated at different time points, which image features comprise at least a time profile of object light scattering intensity and at least a time profile of object position
Data Source
AI summary
Provided herein is a method of detecting microorganisms in liquid samples using image features, such as time profiles of object light scattering intensity and time profiles of object position. Related methods, devices, systems, and other aspects are also provided.