IBS Diagnostic Sensor Using Microbiota and IgG Analysis
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Solution Overview
Problem
Conventional technologies lack effective methods for diagnosing and managing Irritable Bowel Syndrome (IBS) due to inadequate sensitivity and specificity in detecting microbiota-related biomarkers, leading to unreliable diagnosis and treatment monitoring, and are not amenable to automated, cost-effective, repeated use.
Innovation Solution
A decision support tool that utilizes a smart sensor system combining machine-learning classifiers with fecal microbiota profiles, alpha diversity, and serum immunoglobulin G subclass profiles to detect statistically significant alterations, initiating interventions such as notifications or care plan modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional diagnostic methods are used for IBS, then the diagnostic process is simple and inexpensive, but the sensitivity and specificity for detecting microbiota-related biomarkers are inadequate
Solution Approach 1:
The patent combines multiple diagnostic components into an integrated system: microbiota analysis (16S rRNA sequencing), immunoglobulin G subclass measurements, machine learning classifiers, and automated decision support. This merging of previously separate diagnostic approaches enables simultaneous multi-parameter assessment, achieving high sensitivity and specificity while maintaining operational simplicity through automation.
Solution Approach 2:
The patent introduces machine learning classifiers as intermediary components that process raw microbiota and immunoglobulin data, transforming complex biological measurements into clinically actionable diagnostic decisions. This intermediary layer bridges the gap between complex laboratory measurements and simple clinical interpretation, resolving the contradiction between measurement precision and ease of use.
2Reliability
If conventional IBS diagnosis and monitoring methods are used, then the approach is easy to implement, but the reliability of diagnosis and treatment monitoring is poor
Solution Approach 1:
The patent implements automated feedback loops where machine learning classifiers continuously analyze microbiota and immunoglobulin data, compare results against established criteria, and provide real-time diagnostic and prognostic feedback to clinicians. This feedback mechanism ensures consistent application of diagnostic criteria and enables reliable treatment monitoring by tracking changes in microbiota composition and immune responses over time.
Solution Approach 2:
The patent replaces manual diagnostic interpretation with automated machine learning algorithms and decision support systems. This substitution eliminates human variability in diagnostic judgment, standardizes interpretation of complex microbiota data, and provides reproducible, reliable diagnostic and prognostic assessments across different clinical settings.
3Measurement precision
If advanced microbiota analysis and machine learning classifiers are implemented, then diagnostic accuracy improves, but the cost and complexity of repeated automated use increase
Solution Approach 1:
The patent segments the diagnostic process into distinct modular components: sample collection, DNA extraction and 16S rRNA sequencing, immunoglobulin G subclass measurement, machine learning classification, and decision support generation. This segmentation allows each component to be optimized independently and enables flexible implementation strategies, including potential use of standardized commercial kits for DNA extraction and sequencing, reducing overall costs while maintaining diagnostic accuracy.
Solution Approach 2:
The patent employs machine learning techniques that can adapt to varying input data qualities and quantities. The classifiers are trained to recognize diagnostic patterns even with limited or variable-quality microbiota data, allowing the system to maintain high diagnostic accuracy across different clinical settings and resource levels. This parameter adaptability reduces the need for expensive, highly standardized laboratory procedures while preserving diagnostic reliability.
Data Source
AI summary
An improved decision support tool is provided for detecting (or for diagnosing or treating human patient at risk for developing) a functional gastrointestinal condition, such as irritable bowel syndrome (IBS). The decision support tool, which may comprise a smart sensor, determines microbiota diversity, relative abundances of microbial taxa, trends in the relative abundances, and concentrations of immunoglobulin G subclasses, from specimens from the subject, and combines these determined values using a classifier to automatically ascertain whether changes or trends in the values are statistically significant and clinically actionable with respect to diagnosing and managing the subject's condition. The decision support tool may further initiate an intervening action based on this determined joint significance, such as generating an electronic notification, modifying a treatment program, providing a recommendation, automatically allocating health care resources to the patient, or automatically scheduling a consultation with a caregiver.