Deep Learning Chest X-Ray Triage for Infectious Disease Detection
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Solution Overview
Problem
Current methods for detecting infectious respiratory diseases are time-consuming and expensive, often requiring large sample amounts and incubation periods, making them ineffective for timely diagnosis and treatment.
Innovation Solution
An automated X-ray-based triage approach using a deep learning system for detecting and localizing medical abnormalities in chest X-ray scans, combined with algorithmic clinical sample pooling, to rapidly identify infectious respiratory diseases and determine the appropriate treatment type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional molecular testing methods are used for detecting infectious respiratory diseases, then diagnostic accuracy is improved, but testing time and cost increase significantly
Solution Approach 1:
The system performs preliminary X-ray screening and deep learning-based abnormality detection before molecular testing. This preliminary action identifies high-risk patients who need molecular confirmation, allowing parallel processing of molecular tests for multiple patients simultaneously, thereby reducing overall testing time while maintaining diagnostic accuracy
Solution Approach 2:
The diagnostic process is segmented into two independent parallel pathways: (1) X-ray imaging and deep learning analysis for rapid triage, and (2) molecular testing for confirmatory diagnosis. This segmentation allows both processes to occur simultaneously, reducing total testing time while preserving diagnostic accuracy through the confirmatory molecular test
2Measurement precision
If traditional molecular testing is performed for each patient individually, then diagnostic accuracy is maintained, but testing cost and resource consumption increase
Solution Approach 1:
The deep learning system performs preliminary risk stratification using X-ray images, identifying patients with high probability of infectious respiratory diseases. Only these high-risk patients proceed to molecular testing, reducing the number of expensive molecular tests required while maintaining diagnostic accuracy through targeted testing of the most likely cases
Solution Approach 2:
The system uses X-ray imaging as a preliminary copy or surrogate marker for disease presence, which is less expensive than molecular testing. The deep learning model analyzes this cheaper copy to identify candidates for the more expensive molecular test, thereby reducing overall testing costs while maintaining accuracy through selective confirmation
3Measurement precision
If large amounts of clinical sample are collected for molecular testing, then detection sensitivity is improved, but patient comfort and sample collection complexity worsen
Solution Approach 1:
The system performs preliminary enrichment of the clinical sample by concentrating pathogens from larger initial sample volumes before molecular analysis. This preliminary concentration step maintains detection sensitivity by ensuring sufficient pathogen load in the final test, while the actual molecular testing can then be performed on smaller, more manageable sample volumes that are easier to collect and handle
4Measurement precision
If incubation periods are extended for sample processing, then pathogen detection sensitivity is improved, but diagnostic timeliness deteriorates
Solution Approach 1:
The system performs preliminary concentration and pre-processing of clinical samples before molecular testing, enriching pathogen load in advance. This preliminary action allows for reduced incubation or amplification times during actual molecular testing, maintaining detection sensitivity while significantly reducing the time required for pathogen identification and enabling faster diagnostic results
Data Source
AI summary
This disclosure generally pertains to systems and methods for detection of infectious respiratory diseases by implementation of an automated X-rays-based triage approach alongside algorithmic clinical sample pooling for molecular diagnosis. Certain embodiments relate to methods for the development of deep learning algorithms that perform machine recognition of specific features and conditions in chest X-ray imaging data. The chest X-ray imaging data is used to guide the pooling strategy of clinical samples for a molecular test.