3D Quantitative Phase Imaging for Rapid Microorganism Identification
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Solution Overview
Problem
Conventional methods for identifying microorganisms, such as blood culture and biochemical tests, are time-consuming and prone to human error, often taking hours or days to provide accurate results, and are vulnerable to noise artifacts in two-dimensional imaging.
Innovation Solution
The use of three-dimensional quantitative phase imaging (QPI) processed by a 3D convolutional neural network to rapidly and accurately identify microorganisms by generating a 3D refractive index tomogram and predicting the type of microorganisms within a short timeframe, such as under an hour, by analyzing the phase shifts induced in light passing through the specimen.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods such as blood culture and biochemical tests are used to identify microorganisms, then accurate identification can be achieved, but the process takes hours or days and is time-consuming
Solution Approach 1:
The patent replaces conventional mechanical and biochemical identification methods (culturing, biochemical tests) with optical imaging and machine learning algorithms. A phase-contrast microscope captures images of microorganisms, and a neural network processes these images to identify the microorganism type, eliminating the need for time-consuming biological processes while maintaining identification accuracy.
Solution Approach 2:
The patent performs preliminary imaging and analysis steps that enable rapid identification without waiting for microbial growth. By capturing phase-contrast images directly from the specimen and processing them through a trained neural network, the system provides identification results in minutes rather than hours or days, effectively performing the identification action before conventional methods would complete their growth cycles.
2Device complexity
If conventional two-dimensional imaging methods are used, then the imaging process is simple, but the results are vulnerable to noise artifacts and less accurate
Solution Approach 1:
The patent transitions from two-dimensional imaging to three-dimensional quantitative phase imaging. By capturing phase information that encodes optical path length differences, the system creates a third dimension of data that provides more comprehensive structural information about microorganisms. This dimensional enhancement improves identification accuracy by providing more distinctive features for the neural network to analyze, while the phase-contrast microscopy approach keeps the imaging system relatively simple.
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
This approach enables rapid and consistent identification of microorganisms, reducing the reliance on time-consuming culturing and biochemical reactions, and improves accuracy by minimizing noise artifacts, allowing for prompt antibiotic administration and improved clinical outcomes.
Implementation Method 1
Quantitative phase imaging characterizes a specimen by quantifying phase shifts induced in light passing through the specimen
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying the predicted type of one or more microorganisms. In one aspect, a system comprises a phase-contrast microscope and a microorganism classification system. The phase-contrast microscope is configured to generate a three-dimensional quantitative phase image of one or more microorganisms. The microorganism classification system is configured to process the three-dimensional quantitative phase image using a neural network to generate a neural network output characterizing the microorganisms, and thereafter identify the predicted type of the microorganisms using the neural network output.