Neural ARDS Detection Using Thoracic Images and Vital Data
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Solution Overview
Problem
Existing methods for detecting acute respiratory distress syndrome (ARDS) in intensive care patients are unreliable, as they rely solely on X-ray images or physiological parameters, failing to provide early and accurate detection.
Innovation Solution
A computer system utilizing an artificial neural network with multiple subnetworks processes thoracic radiological images and vital data over time to generate an ARDS indicator value, which is compared to a threshold for early detection and notification of potential ARDS.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated detection systems use only X-ray images or only physiological parameters, then the system complexity is reduced, but the detection reliability is insufficient
Solution Approach 1:
The patent combines multiple data sources (X-ray images, physiological parameters, laboratory values) into a unified deep learning model that processes all inputs simultaneously. The system merges radiological image analysis with physiological parameter monitoring to achieve reliable ARDS detection, resolving the contradiction by integrating rather than selecting单一 data source.
Solution Approach 2:
The detection system is segmented into multiple specialized subnetworks: a first subnetwork for processing radiological images, a second subnetwork for physiological parameters, and a third subnetwork for laboratory values. Each subnetwork processes specific data types independently before merging results, allowing complex multi-source analysis while maintaining modular architecture.
2Measurement precision
If deep learning models process multiple data sources simultaneously, then detection accuracy improves, but computational resource requirements increase
Solution Approach 1:
The computational workload is segmented across multiple specialized subnetworks, each optimized for specific data types. This segmentation allows parallel processing of different data sources without requiring a single monolithic model, reducing peak computational demands while maintaining high detection accuracy through comprehensive multi-source analysis.
Data Source
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AI summary
The invention relates to the detection of an acute respiratory distress syndrome in a patient. The subject matter of the present invention relates to a computer system, a method and a computer program product for detecting an acute respiratory distress syndrome.