Biological Sample Microscopy With Selective Machine Learning Composites
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Solution Overview
Problem
Biological sample analysis using dry and fluid samples is hindered by inconsistencies due to manual preparation, technician variability, low-quality images, and challenges in capturing and interpreting images, leading to inaccurate and time-intensive results.
Innovation Solution
A computer-implemented method using machine learning models to capture, analyze, and select subsets of biological sample images, generating composite images that enhance accuracy and efficiency by reducing variability and improving image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual preparation of dry samples is performed by technicians, then sample analysis can be conducted, but the sample composition and physical attributes become inconsistent and inaccurate
Solution Approach 1:
The patent replaces manual mechanical sample preparation with an automated digital imaging system that captures images of samples in their native state. Machine learning algorithms then process these images to identify and analyze cellular components, eliminating the need for manual smearing and drying while maintaining or improving sample integrity and consistency.
Solution Approach 2:
The system creates digital copies (images) of the sample as it exists in its native state, allowing multiple analyses without physical manipulation. These digital replicas can be processed, stored, and re-analyzed without altering the original sample, ensuring consistent representation of sample characteristics.
2Ease of operation
If technicians manually analyze biological sample images, then characteristics can be determined, but the process is time-intensive and results vary between technicians
Solution Approach 1:
The patent replaces manual visual analysis with automated machine learning models that process digital images. These algorithms objectively identify cellular components, measure characteristics, and generate results consistently across different samples and time periods, eliminating inter-technician variability while dramatically increasing analysis throughput.
Solution Approach 2:
The system enables self-service analysis where the machine learning models autonomously perform identification, measurement, and characterization of cellular components without human intervention. The automated pipeline processes images through multiple algorithmic stages to deliver comprehensive analytical results.
3Loss of information
If all captured images are transmitted and analyzed, then complete data is available, but data volume is large and processing is inefficient
Solution Approach 1:
The patent extracts only the most relevant features and characteristics from captured images using machine learning algorithms. Instead of transmitting and analyzing entire image datasets, the system identifies key cellular components and their properties, extracting essential information while discarding redundant data, thereby reducing processing time and computational resources.
Solution Approach 2:
The system segments the analysis process into distinct stages: initial image capture, preliminary filtering to identify regions of interest, detailed analysis of selected areas, and synthesis of results. This hierarchical segmentation allows efficient processing by focusing computational resources on the most informative portions of the data.
Data Source
AI summary
A computer-implemented method for interrogating a sample with a microscopy device is disclosed. The computer-implemented method comprises capturing, by a microscopy device, one or more images of a biological sample. The computer-implemented method also comprises inputting the one or more images into one or more machine learning models and identifying, in the one or more images of the biological sample, via the one or more machine learning models, a plurality of images of a cell type. The computer-implemented method further comprises selecting, by the one or more machine learning models, a subset of the plurality of images of the cell type for transmission. The computer-implemented method also comprises, in response to selecting the subset of the plurality of images of the cell type for transmission, generating, via the one or more machine learning models, one or more composite images, wherein the one or more composite images comprise a representation of at least one characteristic of the subset of the plurality of the images of the cell type, and transmitting, to a computing device, the composite image.