Oral Cavity Characterization System Using Microbiome Sequencing
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Solution Overview
Problem
Current methods for characterizing mouth-associated conditions, such as gingivitis and halitosis, are limited by the need for physical inspections, inefficiencies in genetic sequencing, and challenges in processing large microbiome datasets, leading to incomplete understanding and ineffective therapies.
Innovation Solution
A system and method that utilize advanced computer technologies, including artificial intelligence and machine learning, to analyze microbiome composition and functional diversity data from biological samples, generating personalized characterization and therapy models for mouth-associated conditions, enabling remote diagnostics and treatment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic methods (questionnaires, visual inspection) are used, then the system is simple and easy to operate, but the diagnostic accuracy and ability to detect early signs of mouth-associated conditions is insufficient
Solution Approach 1:
The system segments the diagnostic process into multiple independent modules: light source module, imaging module with multiple sensors, processing module, and display module. Each module performs a specific function, allowing the complex diagnostic system to be built from simpler, manageable components while maintaining high diagnostic accuracy through coordinated operation of all segments.
Solution Approach 2:
The system employs multi-functional imaging capabilities that can detect various mouth-associated conditions (caries, periodontal disease, oral cancer, dry mouth) using a single integrated platform. The same hardware system performs multiple diagnostic functions by analyzing different parameters (optical properties, temperature, moisture content) from the oral cavity, eliminating the need for separate diagnostic devices for each condition.
2Reliability
If comprehensive imaging and analysis systems are implemented, then early detection capability improves, but the ease of operation and accessibility for general use deteriorates
Solution Approach 1:
The system incorporates automated image capture and analysis functions that operate without requiring specialized operator skills. The processing module automatically analyzes captured images to detect early signs of mouth-associated conditions, generating diagnostic results without manual intervention. This self-service capability allows general users to access reliable early detection without needing professional training.
Solution Approach 2:
The system replaces complex manual diagnostic procedures with automated optical imaging and computer-based analysis. Instead of requiring practitioners to perform manual examinations and interpret findings, the system uses light-based imaging captured by sensors and processed by algorithms to automatically detect conditions, thereby maintaining high reliability while improving ease of operation.
3Loss of information
If multiple imaging parameters and sensors are used, then the information obtained about mouth-associated conditions is more comprehensive, but the device complexity and cost increase
Solution Approach 1:
The system merges multiple sensing functions into a single integrated imaging platform. Different sensors (optical sensors, temperature sensors, moisture sensors) are combined within one device to capture multiple parameters (optical properties, thermal state, hydration levels) simultaneously during a single oral examination, providing comprehensive information without requiring separate diagnostic devices for each parameter.
Solution Approach 2:
The imaging system serves multiple diagnostic purposes through a unified platform. The same optical imaging system detects various conditions (caries, periodontal disease, oral cancer, dry mouth) by analyzing different aspects of the captured data, thereby reducing the need for multiple specialized sensors and devices while maintaining complete diagnostic information.
Data Source
AI summary
Embodiments of a system and method for characterizing a mouth-associated condition in relation to a user can include one or more of: a handling network operable to collect containers including material from a set of users, the handling network including a sequencing system operable to determine microorganism sequences from sequencing the material; a microbiome characterization system operable to determine microbiome composition data and microbiome functional diversity data based on the microorganism sequences, collect supplementary data associated with the mouth-associated condition for the set of users, and transform the supplementary data and features extracted from the microbiome composition data and the microbiome functional diversity data into a characterization model for the mouth-associated condition; and/or a therapy system operable to promote a treatment to the user based on characterizing the user with the characterization model in relation to the mouth-associated condition.