Lung Fibrosis Quantification via Neural Network Segmentation
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Solution Overview
Problem
Current methods for assessing lung fibrosis and disease progression in clinical trials are inefficient and subjective, relying heavily on manual analysis and variable techniques such as Forced Vital Capacity (FVC) measurements and visual assessment of CT scans.
Innovation Solution
A machine learning approach using trained neural network models to compute a lung fibrosis metric based on lung imaging data, specifically computed tomography (CT) scans, by segmenting lung and fibrosis regions and calculating a fibrosis volume metric.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis and visual assessment of CT scans are used to assess lung fibrosis, then clinical expertise and interpretability are maintained, but measurement precision and consistency deteriorate due to subjectivity and technician variability
Solution Approach 1:
The patent introduces an automated image analysis system as an intermediary between the CT scan data and clinical interpretation. This system processes lung imaging data through trained machine learning models to generate quantitative fibrosis metrics, serving as a mediator that bridges raw imaging data and clinical decision-making while improving measurement precision and reducing technician variability
Solution Approach 2:
The patent replaces the manual mechanical process of visual assessment by technicians with an automated computational system. The machine learning models automatically segment lung tissue, identify fibrosis patterns, and calculate quantitative metrics, substituting human visual inspection with algorithm-based analysis to improve consistency and precision
2Reliability
If Forced Vital Capacity (FVC) breathing tests are used to measure lung function, then a standard clinical approach is provided, but reliability deteriorates due to technician variability and patient condition on the test day
Solution Approach 1:
The patent creates a digital copy or surrogate measure of lung function through quantitative fibrosis metrics derived from CT scan analysis. Instead of relying on the variable FVC breathing test, the system generates a stable quantitative representation of lung fibrosis from imaging data, providing a more reliable measure that is not affected by technician variability or patient condition on the test day
3Measurement precision
If automated machine learning models are implemented to quantify lung fibrosis, then measurement precision and consistency improve, but device complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on large datasets of annotated lung CT scans before deployment. The models are pre-trained to recognize fibrosis patterns, segment lung tissue, and generate quantitative metrics. This preliminary training phase enables the system to provide accurate, consistent measurements in clinical use without requiring complex real-time processing or manual intervention during actual assessments
Data Source
AI summary
A system and method for computing a lung fibrosis metric are described. The system has an input interface to receive initial lung imaging data for the patient, a trained neural network lung segmentation model to generate lung segmentation data from the initial lung imaging data, a fibrosis model pre-processor to apply the lung segmentation data to the lung imaging data to produce modified lung imaging data, a trained neural network lung fibrosis model to generate fibrosis segmentation data from the modified lung imaging data, a fibrosis model post-processor to process the fibrosis segmentation data in combination with the lung segmentation data to generate labelled voxel data, a fibrosis metric processor to use the labelled voxel data to compute a fibrosis volume metric for the patient, and an output interface to provide the fibrosis volume metric as the lung fibrosis metric for the patient.