Automated Sample Classification for Infectious Disease Testing
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Solution Overview
Problem
Current laboratory testing procedures for infectious diseases, such as SARS-CoV-2, have lengthy turnaround times, which hinder timely identification and isolation of infected individuals, and lack efficient data management systems to predict and manage infection hotspots effectively.
Innovation Solution
A method and system for automated biological sample testing using robotic extraction and classification of genetic material, combined with machine-learning models to identify disease agents, which includes a computing device for receiving specimens with unique identifiers, extracting genetic sequences, and determining test results, while managing human subject descriptive data for efficient data tracking and notification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated robotic extraction and machine-learning classification are implemented, then productivity and turnaround time are improved, but device complexity increases
Solution Approach 1:
The system segments the testing process into distinct automated modules: robotic sample extraction, optical identifier scanning, machine-learning disease classification, and separate result determination. Each module operates independently but integrates through standardized interfaces, enabling high throughput while managing complexity through modular design.
Solution Approach 2:
The patent introduces an intermediary machine-learning classifier that bridges raw genetic sequence data and disease agent identification. This intermediary layer processes extracted genetic material through trained models to determine disease presence, decoupling the complexity of genetic analysis from the final diagnostic decision and enabling scalable automation.
2Loss of information
If comprehensive human subject descriptive data is collected and classified, then data management quality improves, but loss of time in data processing increases
Solution Approach 1:
The system performs preliminary classification of human subject descriptive data using machine-learning models trained on historical data. By pre-processing and pre-classifying descriptive information alongside genetic sequencing, the system prepares data structures in advance that accelerate final disease agent identification and reduce overall processing time.
Solution Approach 2:
The patent transforms human subject descriptive data from unstructured text into structured classified parameters through machine-learning processing. This parameter transformation enables efficient data storage, retrieval, and analysis, improving data management quality while reducing processing time through standardized data representations.
Data Source
AI summary
A method of classifying sample data for robotically extracted samples is disclosed. A specimen is received from a human subject with a potential infection of a first disease agents or a plurality of disease agents. The specimen includes genetic material collected from a human subject using a collection device and stored in a collection carrier. The specimen includes a unique identifier on the collection carrier. The unique identifier contains human subject descriptive data. The method classifies the human subject descriptive data to identify a second disease agent. The method extracts a sequence of genetic material from the specimen using an automated robot. The method determines a test result for the first disease agent as a function of the sequence of genetic material. A system comprising a computer device configured to classify sample data for robotically extracted samples is also disclosed.