Animal Pathogen Detection Using Metadata and Molecular Signatures
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Solution Overview
Problem
Current animal health diagnostics lack a systemic approach for efficiently evaluating and identifying harmful agents, such as pathogens, to enable productive early intervention or prevention, and existing systems fail to leverage genomic sequence for comprehensive pathogenic burden analysis.
Innovation Solution
A system comprising assay kits, hardware, databases, and AI/ML for automated analysis of biological and environmental information to detect, identify, and predict pathogens, generating actionable intelligence reports in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional diagnostic methods are used to identify pathogens, then the diagnostic process is simpler, but the time required exceeds 24 hours and comprehensive pathogenic burden analysis cannot be achieved
Solution Approach 1:
The diagnostic system is segmented into distinct functional modules: sample processing unit, nucleic acid extraction unit, sequencing unit, AI/ML analysis unit, and reporting unit. Each module performs a specific function, allowing parallel processing and reducing overall diagnostic time while maintaining comprehensive analysis capability.
Solution Approach 2:
An AI/ML system serves as an intermediary between raw sequencing data and diagnostic conclusions. The AI/ML system processes molecular signatures, integrates sample metadata, and generates actionable intelligence reports, enabling rapid interpretation of complex data without requiring extensive manual analysis.
2Measurement precision
If comprehensive molecular analysis of multiple microorganisms is performed, then pathogenic burden analysis is improved, but the cost and complexity of testing increase
Solution Approach 1:
The sequencing-based diagnostic system is designed to universally detect and identify multiple types of microorganisms (bacteria, viruses, fungi, parasites) simultaneously through a single assay platform. The system can analyze various sample types (swabs, blood, tissue) and provides comprehensive pathogenic burden analysis across diverse pathogens without requiring separate specialized tests for each organism type.
Solution Approach 2:
The system changes the parameter of detection from targeted, pathogen-specific testing to broad, sequence-based identification. By using genomic sequencing and AI/ML analysis of molecular signatures, the system can identify pathogens based on their genetic parameters rather than relying on multiple separate antigen or antibody tests, reducing overall assay complexity while improving comprehensive detection capability.
3Loss of information
If genomic sequencing is used to identify pathogens, then comprehensive pathogenic analysis is achieved, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing extensive sample metadata (animal demographics, clinical signs, environmental factors, treatment history) before sequencing analysis. The AI/ML system is pre-trained on large datasets of pathogen sequences and clinical outcomes, enabling rapid interpretation of sequencing results and generation of diagnostic conclusions within 24 hours rather than requiring extended analysis time.
Solution Approach 2:
The system replaces manual, time-consuming analysis of genomic data with automated AI/ML algorithms. The artificial intelligence system automatically processes sequencing data, compares molecular signatures against reference databases, integrates metadata, and generates actionable intelligence reports, reducing analysis time from days to hours while maintaining comprehensive pathogen identification.
4Productivity
If automated AI/ML systems are implemented for real-time analysis, then diagnostic efficiency is improved, but the initial system setup and infrastructure requirements increase
Solution Approach 1:
The AI/ML system is designed to autonomously process diagnostic data without requiring extensive human computational resources. The system self-manages data processing, pathogen identification, and report generation, reducing the need for large teams of analysts and minimizing ongoing computational resource requirements while maintaining high diagnostic throughput.
Data Source
AI summary
Provided herein are systems and methods for animal health assessment, surveillance, and enhancement. For example, provided here are systems, devices, and processes that collect and analyze, in an efficient and high-throughput manner, biological information, environmental information, and demographic information about animal subjects to detect, identify, predict, and/or surveil adverse-health-causative or-associated agents, such as pathogens, and to select and identify suitable interventions.