Oral Microbiome Profiling for Early ASD Risk Assessment
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Solution Overview
Problem
Current diagnostic methods for autism spectrum disorder (ASD) are unreliable, temporary, and lack a comprehensive, objective approach, while therapeutic interventions focus on behavior amelioration rather than cure, and oral microbiome analysis is primarily subjective and not economically viable.
Innovation Solution
A method involving paired extraction and quantification of site-specific microbial sequences from saliva and dental plaque samples, using ensemble machine learning to classify ASD risk and develop personalized microbial cocktails for treatment, based on a predefined ensemble of mathematical formulas and microbial sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If psychiatric or behavioral evaluations and checklists are used for ASD diagnosis, then diagnostic procedures can be implemented, but the reliability and accuracy of early diagnosis deteriorate
Solution Approach 1:
The patent replaces subjective psychiatric and behavioral evaluations with an objective computational system that uses machine learning algorithms to analyze oral microbiome data. This substitution transforms the diagnostic process from a manual, subjective assessment to an automated, data-driven system, thereby improving reliability while managing complexity through algorithmic processing.
Solution Approach 2:
The patent introduces oral microbiome analysis as an intermediary biomarker system between the subject and the diagnostic conclusion. Instead of directly assessing behavioral traits, the system uses microbial composition in oral samples as an objective intermediary indicator that correlates with ASD risk, enabling more reliable and early diagnosis.
2Measurement precision
If existing diagnostic methods are used, then diagnostic procedures can be performed, but measurement precision and objectivity deteriorate
Solution Approach 1:
The patent replaces manual psychiatric assessments with an automated machine learning-based computational system. The system objectively processes oral microbiome data through predefined algorithms, eliminating human subjectivity and improving measurement precision. The automated nature of the system maintains ease of operation despite the sophisticated analysis performed.
3Reliability
If oral microbiome analysis is performed using conventional methods, then microbial composition can be studied, but diagnostic reliability and economic viability deteriorate
Solution Approach 1:
The patent extracts and focuses on specific key microbial taxa from complex oral microbiome samples that have been identified as most relevant to ASD diagnosis. By concentrating analysis on these critical microbes rather than attempting to analyze all microbial components, the system achieves high diagnostic reliability while reducing computational and analytical costs, thereby improving economic viability.
Solution Approach 2:
The patent applies different analytical approaches to different microbial taxa based on their relevance to ASD. Instead of uniform analysis of all microbes, the system identifies and prioritizes specific microbial groups that show significant associations with ASD, allocating more analytical resources to these high-value targets while simplifying analysis of less relevant components.
Data Source
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AI summary
This disclosure relates more particularly to risk assessment of autism spectrum disorder (ASD) present in the subject and designing a personalized recommendation for the same. Current diagnostic tools and procedures, though abundant in numbers, are all based on psychiatric or behavioral evaluations, checklists and associated statistical inferences, which highlight the inherent limitation in making a reliable and early diagnosis. The present disclosure makes use of oral microbial samples of both saliva and dental plaque. The present disclosure involves a paired extraction and quantification of site-specific unique microbial sequences pertaining to the oral microbial samples of an ASD subject and subsequent classification of the subject under the ASD risk category using a metric based on a predefined ensemble of mathematical formulas. Further, a guided development of personalized microbial cocktail(s) is then designed based on the most relevant formula-set for the subject.