Digital Microscopy with Adaptive Lighting and Machine Learning
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Solution Overview
Problem
Manual preparation of dry biological samples for microscopy analysis leads to physical alteration and distortion, resulting in inconsistent and inaccurate sample characteristics, which affects the accuracy and precision of analytical results.
Innovation Solution
A method involving capturing initial images to determine stain intensity, modifying the light source intensity based on this determination, capturing secondary images, and using machine learning models to analyze these images and identify sample characteristics, thereby reducing human error and enhancing analysis accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual preparation of dry samples is performed by technicians, then the sample can be prepared for microscopy analysis, but the sample is physically altered and distorted resulting in inconsistent and inaccurate characteristics
Solution Approach 1:
The patent replaces the manual mechanical smearing process with an automated digital microscopy system that captures images of the sample in its natural state. The system uses digital image processing and machine learning algorithms to analyze sample characteristics without physical manipulation, thereby eliminating the mechanical alteration and distortion caused by manual preparation techniques.
Solution Approach 2:
The patent creates a digital copy of the sample through high-resolution imaging rather than requiring physical manipulation of the actual sample. Multiple images are captured from different angles and focal planes, creating a comprehensive digital representation that preserves sample characteristics without physical alteration. This digital copy is then analyzed computationally to determine sample properties.
2Ease of operation
If technicians manually analyze images of biological samples, then characteristics can be determined, but the analysis is time-intensive and varies from technician to technician
Solution Approach 1:
The patent replaces manual visual analysis by technicians with an automated machine learning-based image analysis system. The system uses trained algorithms to automatically identify and characterize sample features, eliminating the time-consuming manual review process while ensuring consistent, reproducible results across different samples and operators.
Solution Approach 2:
The system performs self-analysis through automated machine learning models that independently evaluate captured images and determine sample characteristics. The algorithm processes images, identifies relevant features, and generates diagnostic information without requiring human intervention for each sample analysis, thereby dramatically reducing analysis time while maintaining or improving accuracy.
3Device complexity
If low-resolution or low-contrast images are used, then the imaging process is simpler, but the technician's ability to analyze relevant characteristics is hindered
Solution Approach 1:
The patent employs dynamic image capture with adjustable lighting conditions and multiple focal planes. The system adapts illumination intensity and contrast enhancement based on sample characteristics, capturing images with optimized quality for detailed analysis. This dynamic adjustment of imaging parameters ensures high measurement precision without requiring overly complex fixed imaging equipment.
Solution Approach 2:
The patent captures images across multiple focal planes and angular perspectives, adding dimensional information to the analysis. By acquiring data in three-dimensional space rather than a single two-dimensional plane, the system enhances the ability to identify and characterize sample features, improving measurement precision through multi-dimensional image data processing.
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 minimizes sample alteration, enhances image quality, and improves the accuracy and precision of sample characterization, leading to more reliable analytical results.
Implementation Method 1
capturing one or more first images from an imaging sensor
Implementation Method 2
capturing one or more first images from an imaging sensor
Implementation Method 3
modifying an intensity of a light source
Data Source
AI summary
A method for interrogating a sample with a microscopy analyzer is disclosed. The method includes capturing, by an imaging sensor, of the microscopy analyzer, one or more first images, determining a stain intensity, modifying an intensity of a light source of the microscopy analyzer, based at least in part on the determined stain intensity, in response to modifying the intensity of the light source, capturing one or more second images from the imaging sensor, inputting the one or more first images and the one or more second images into one or more machine learning models, identifying, via the one or more machine learning models, one or more characteristics of the one or more first images and the one or more second images, and transmitting instructions that cause a graphical user interface to display a graphical indication of the one or more characteristics.