Microbiome Sequencing for Non-Invasive IBD Detection
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Solution Overview
Problem
Current methods for diagnosing inflammatory bowel disease (IBD) are invasive, costly, and have variable accuracy, leading to delayed diagnosis and infrequent monitoring, necessitating the development of non-invasive, low-cost, and rapid detection methods.
Innovation Solution
A method involving preprocessing of 16S rRNA gene sequencing data from biological samples using filtering, normalization, batch effect reduction, and feature engineering, followed by machine learning model training and testing to determine the likelihood of IBD, utilizing techniques such as alpha diversity, dysbiosis index, and microbiome health indices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional diagnostic methods (blood tests, endoscopies, fecal calprotectin) are used for IBD detection, then diagnostic accuracy can be achieved, but the methods are invasive, costly, and lead to delayed diagnosis
Solution Approach 1:
The patent replaces invasive mechanical diagnostic procedures (endoscopies, blood draws) with non-invasive microbiome sequencing analysis. The system uses computational methods to analyze microbial community data from stool samples, substituting physical invasion with bioinformatic processing to achieve diagnostic accuracy.
Solution Approach 2:
The patent introduces the gut microbiome as an intermediary biomarker for IBD detection. Instead of directly measuring inflammatory markers through invasive means, the system uses the microbiome community structure as a mediator that reflects disease state, enabling non-invasive diagnosis through sequencing analysis.
2Reliability
If traditional diagnostic methods are used for IBD detection, then some diagnostic capability is achieved, but the methods are costly and lead to infrequent monitoring
Solution Approach 1:
The patent employs cost-effective microbiome sequencing approaches that use readily available stool samples instead of expensive endoscopic equipment or specialized blood test reagents. The methodology leverages standard sequencing technologies and open-source bioinformatic tools to reduce per-test costs, enabling more frequent monitoring.
Solution Approach 2:
The system uses naturally present microbiome DNA in stool samples as the diagnostic material, requiring no additional contrast agents, radioactive tracers, or expensive reagents. The body's own microbial community serves as the diagnostic substrate, eliminating the need for costly external substances.
3Reliability
If complex preprocessing steps are applied to sequencing data, then model generalizability is improved, but computational complexity increases
Solution Approach 1:
The patent divides the complex preprocessing task into distinct modular steps: quality filtering, normalization, batch effect correction, and feature engineering. Each step addresses a specific source of variability independently, making the overall complex process more manageable and reproducible across different datasets.
Solution Approach 2:
The patent applies preprocessing transformations to the training data before model construction, including normalization and batch effect correction. These preliminary actions prepare the data in advance to reduce variability sources before the model learns patterns, improving generalizability without requiring the model itself to be overly complex.
Data Source
AI summary
Methods, devices, and systems for detecting inflammatory bowel disease are described herein. The method includes obtaining a biological sample of a subject, determining sequencing data from the biological sample, and preprocessing the sequencing data. The preprocessing includes filtering the sequencing data to remove rare features, normalizing the filtered sequencing data to remove sequencing coverage variability, and batch effect reducing the filtered and normalized sequencing data. The method further includes calculating a likelihood of inflammatory bowel disease with a machine learning model using the preprocessed data as inputs.