AI CAD System for COVID-19 Lung Severity Assessment
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Solution Overview
Problem
Current CAD systems for assessing lung function in COVID-19 patients are limited in their ability to accurately differentiate between severe and mild/moderate cases due to variations in X-ray image quality.
Innovation Solution
A novel CAD system utilizing AI and machine learning techniques that extracts rotation, scaling, and translation invariant X-ray image markers to capture both local and global features of the lung, enabling objective differentiation between severe and non-severe COVID-19 cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional CAD systems are used to assess lung function in COVID-19 patients, then the assessment process can be performed, but the accuracy in differentiating between severe and mild/moderate cases is limited due to variations in X-ray image quality
Solution Approach 1:
The system transforms X-ray images into a standardized representation space using domain adaptation techniques, changing the parameter space in which disease severity is assessed. This normalization process adjusts for variations in image quality, exposure, and acquisition parameters, enabling consistent and accurate severity differentiation across diverse X-ray datasets.
Solution Approach 2:
The patent introduces an intermediary domain adaptation layer between the X-ray images and the disease severity assessment model. This intermediary component acts as a mediator that harmonizes the input images, removing the influence of acquisition variations and ensuring that the assessment relies on genuine disease severity markers rather than image quality artifacts.
2Measurement precision
If AI-based methods are applied to detect COVID-19, then detection capability is improved, but the system complexity increases
Solution Approach 1:
The system segments the AI-based assessment into distinct functional modules: image acquisition, domain adaptation, feature extraction, and severity classification. This segmentation allows each component to be optimized independently and facilitates easier implementation and maintenance while maintaining high detection capability through specialized algorithms in each module.
Solution Approach 2:
The patent replaces complex manual radiological assessment with automated AI-based computational methods. The mechanical system of human radiologist interpretation is substituted with digital image processing and machine learning models, reducing subjectivity and increasing consistency while managing complexity through software-based solutions that can be updated and refined.
3Loss of information
If chest radiography is used for evaluation, then it provides useful diagnostic information, but it cannot reliably distinguish between severe and mild cases due to image quality variations
Solution Approach 1:
The system adds a new dimension to the analysis by transforming 2D X-ray images into a standardized representation space through domain adaptation. This dimensional transformation creates an additional layer of processing that separates genuine disease severity information from image quality variations, enabling precise severity differentiation while preserving all diagnostic information from the original images.
Data Source
AI summary
Assessment of pulmonary function in coronavirus patients includes use of a computer aided diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data from a patient, extracts imaging markers, and grades disease severity based at least in part on the extracted imaging markers, thereby distinguishing between higher risk and lower risk patients.