Real-Time Pathogen Detection With Two-Stage Read Classification
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Solution Overview
Problem
Existing methods for pathogen detection in samples, particularly environmental samples, lack the necessary sensitivity and accuracy to avoid false positives, which can lead to unnecessary alerts or actions, and are computationally inefficient due to the low number of pathogen reads.
Innovation Solution
A two-stage classification process using different algorithms, where a first classification algorithm identifies potential pathogens and a second, more accurate algorithm confirms the classification, with a confidence score incrementing based on matching and confirmation, and additional analysis to reduce false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single classification algorithm is used for pathogen detection, then the detection process is simple and fast, but the false positive rate is high and sensitivity is insufficient
Solution Approach 1:
The pathogen detection process is segmented into two distinct classification stages. The first classification algorithm performs initial pathogen identification, and the second classification algorithm performs confirmation. This segmentation allows each algorithm to be optimized for its specific function, improving overall detection accuracy while managing complexity through modular design.
Solution Approach 2:
A confidence score mechanism serves as an intermediary between the two classification algorithms. The confidence score accumulates evidence from both classification results, acting as a mediator that integrates the outputs of both algorithms to produce a final pathogen detection decision. This intermediary mechanism enables the system to leverage both algorithms' strengths while maintaining a structured decision-making process.
2Measurement precision
If a more accurate classification algorithm is used, then the false positive rate decreases, but the computational efficiency decreases
Solution Approach 1:
The second, more accurate classification algorithm is applied selectively only to sequence reads that are flagged as potential pathogens by the first algorithm, rather than being applied to all sequence reads. This partial application of the more computationally intensive algorithm maintains high detection accuracy for critical cases while preserving overall computational efficiency by limiting the algorithm's runtime burden.
Solution Approach 2:
Different classification algorithms are applied to different subsets of sequence reads based on their pathogenicity risk. The first algorithm handles routine sequencing data with standard accuracy requirements, while the second high-accuracy algorithm is applied locally to suspicious cases that require confirmatory analysis. This local quality differentiation optimizes the balance between accuracy and computational cost.
3Reliability
If the number of pathogen reads is low, then the sensitivity of detection decreases, but using more aggressive detection thresholds increases false positives
Solution Approach 1:
The confidence score mechanism provides feedback integration between the two classification algorithms. When the first algorithm identifies a potential pathogen, the confidence score is updated, and the second algorithm's results are fed back to further adjust the confidence score. This feedback loop allows the system to accumulate evidence across both classification stages, enabling reliable detection of low-abundance pathogens while maintaining strict criteria that prevent false positive alerts.
Data Source
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AI summary
We describe a method and system for detecting pathogens in a sample, particularly an air sample. The method is designed to run in real-time alongside sequencing processing of the sample so that results are quickly available to a user. Each input sequence read in the plurality of sequence reads is compared using a first classification algorithm and assigned to a taxon. An input sequence is classified as potentially pathogenic when the taxon to which the input sequence read is assigned corresponds to a pathogen taxon. Each input sequence read classified as potentially pathogenic is compared using a second classification algorithm to a plurality of target sequences in a second database which is selected based on the pathogen taxon. A confidence score is used as part of the decision-making process to decide if an individual read represents a genuine match to a pathogen.