CT Lung Image Scoring for ILD Progression Prediction
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Solution Overview
Problem
Current methods for measuring lung function and predicting the progression of interstitial lung diseases (ILDs) are plagued by variability and ethical challenges, making it difficult to design effective clinical trials and identify therapeutic responses.
Innovation Solution
A computer-implemented method using image analysis and machine learning to quantify ILD extent and predict progression by segmenting lung images, applying weightings to identified structures based on their relative position to the lung periphery, and utilizing a convolutional neural network (CNN) to identify relevant features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If forced vital capacity (FVC) testing is used to measure lung function and predict ILD progression, then lung function can be quantified, but measurement variability of at least 10% prevents accurate prognostic information and reliable clinical trial design
Solution Approach 1:
The patent replaces the mechanical FVC testing system with an image-based computational system. CT scan images are processed through automated algorithms that quantify fibrotic structures, replacing the mechanical spirometry approach with a visual-analytical method that avoids the measurement variability inherent in patient-performed breathing maneuvers
Solution Approach 2:
The patent creates a digital representation (copy) of lung structure from CT images, allowing repeated measurement and analysis without requiring repeated physical testing. This digital model can be analyzed multiple times with consistent results, eliminating the variability of repeated FVC tests while maintaining measurement capability
2Reliability
If placebo-controlled trials are performed for IPF patients, then therapeutic efficacy can be rigorously tested, but it is no longer ethical to perform such trials given the poor prognosis and lack of prognostic stratification
Solution Approach 1:
The patent performs preliminary stratification of patients into high-risk and low-risk groups using image analysis before clinical trials begin. This preliminary action identifies which patients are most likely to deteriorate, allowing ethical enrollment decisions to be made in advance, so that only appropriately stratified patients are assigned to placebo groups
Solution Approach 2:
The patent segments the patient population into distinct risk categories based on quantitative image analysis of fibrotic structures. This segmentation divides the homogeneous IPF population into heterogeneous subgroups with different prognoses, enabling ethical trial design where placebo control is only applied to low-risk patients who have alternative treatment options
3Adaptability or versatility
If FVC change is used as the primary endpoint in ILD clinical trials, then a standardized measure is available, but the measurement variability of at least 10% makes it difficult to demonstrate incremental improvements above standard of care
Solution Approach 1:
The patent changes the measurement parameter from functional capacity (FVC) to structural quantification (fibrotic burden on CT images). This parameter change shifts from measuring physiological function to measuring anatomical structure, providing a more precise and less variable endpoint that can detect smaller therapeutic effects
4Adaptability or versatility
If individual variability in disease trajectory is acknowledged in IPF patients, then personalized prognosis becomes possible, but currently it is not possible to provide prognostic information at diagnosis
Solution Approach 1:
The patent performs preliminary prognostic assessment at the time of diagnosis using baseline CT image analysis. By analyzing fibrotic structures before disease progression occurs, the system captures individual variability early, providing personalized prognosis information at diagnosis rather than waiting for subsequent FVC measurements
Data Source
AI summary
A computer implemented method for quantifying and predicting the progression of interstitial lung disease is disclosed herein. The method comprises obtaining at least one image based on a scan of at least a part of a patient's lung, segmenting the image to obtain a lung mask defining the periphery of the lung and to identify structures within the lung mask, and applying a weighting to the identified structures based on the relative position of the structures relative to the lung periphery to obtain a weighted score. The method may then comprise quantifying the extent of interstitial lung disease, and/or predicting the progression of interstitial lung disease, based on the weighted score.