Fecal Image Analysis for Non-Invasive Metabolite Profiling
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Solution Overview
Problem
Current metabolomics analysis techniques are inefficient and invasive, lacking a non-invasive, routine method for analyzing metabolite profiles in subjects.
Innovation Solution
A method and system utilizing a trained machine learning model to analyze a digital image of a fecal sample, extracting features to determine metabolite profiles, allowing for rapid, efficient, and cost-effective monitoring of metabolite balance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional metabolomics analysis techniques (Mass Spectrometry, NMR, sequencing) are used, then measurement precision of metabolite profiles is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent uses digital images of fecal samples as a simplified copy or representation of the actual metabolite data, replacing complex laboratory analysis. The image-based system captures visual characteristics that correlate with metabolite profiles, providing an accessible proxy that eliminates the need for expensive and complex analytical instruments while maintaining diagnostic utility
Solution Approach 2:
The patent replaces mechanical and chemical analysis systems (Mass Spectrometry, NMR) with an optical/image-based system. Instead of using complex physical and chemical processes to analyze metabolites, the system uses digital image processing and machine learning to extract metabolite profile information from visual characteristics of fecal samples
2Measurement precision
If traditional metabolomics analysis techniques are used, then measurement precision is improved, but ease of operation deteriorates due to invasive procedures and specialized equipment requirements
Solution Approach 1:
The patent enables subjects to perform their own sample collection and initiate the analysis process without requiring specialized laboratory personnel or equipment. Subjects can collect fecal samples at home, capture images using standard devices, and the system automatically processes the data through machine learning models, making the entire process self-service oriented and eliminating the need for invasive procedures
Solution Approach 2:
The patent replaces the need for complex laboratory procedures with simple image capture. Instead of requiring subjects to undergo invasive sampling and transport samples to specialized facilities, the system uses digital images as a copy that preserves the necessary information for metabolite profile analysis, dramatically simplifying the operational process
3Reliability
If frequent metabolite profile monitoring is implemented, then health detection capability is improved, but loss of time and resources increases with traditional methods
Solution Approach 1:
The patent employs machine learning models that have been pre-trained on extensive datasets to perform rapid classification and metabolite quantification. The preliminary training phase allows the system to make instant predictions on new samples without requiring time-consuming laboratory analysis, enabling frequent monitoring with minimal time investment per sample
Solution Approach 2:
The patent replaces time-consuming mechanical and chemical analysis processes with instantaneous digital image processing. The machine learning system can analyze multiple samples in parallel and provide results in real-time, eliminating the delays inherent in traditional laboratory methods and enabling frequent, routine monitoring
Data Source
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AI summary
A method and system for analyzing and/or estimating a metabolite profile of a subject. A digital image of a sample of feces of the subject is received by one or more processors. The digital image and/or one or more features extracted from the digital image is provided as input to a trained machine learning model which is configured to output a classification based on said input digital image and/or one or more features extracted from the digital image. Data indicative of one or more properties of the metabolome of the subject based on the output image classification is determined by the one or more processors.