Machine Learning Lung Image Segmentation for IPF Progression
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Solution Overview
Problem
Current methods for assessing lung disease progression, particularly in idiopathic pulmonary fibrosis (IPF), are inefficient and inaccurate due to reliance on subjective measurements like Forced Vital Capacity (FVC), which vary with technician skill and patient condition, and lack precise quantification of lung changes.
Innovation Solution
A machine learning approach using segmented lung images from computed tomography (CT) scans to train airway and lung lobe models, enabling automated and precise segmentation and measurement of lung volumes and airway changes, thereby providing a more objective assessment of disease progression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If Forced Vital Capacity (FVC) breathing test is used to assess lung disease progression, then the assessment can be performed in clinical settings, but the measurement precision is poor due to variability from technician skill and patient condition
Solution Approach 1:
The patent replaces the mechanical/manual FVC breathing test with an automated machine learning-based image analysis system. The system uses trained models to automatically segment lung images and quantify disease progression, eliminating the need for manual technician interpretation and reducing variability associated with human operation.
Solution Approach 2:
The patent creates a virtual copy of the lung assessment process through machine learning models trained on segmented lung images. These models replicate and automate the disease progression assessment, producing consistent measurements that can be applied across different clinical settings without relying on technician skill.
2Measurement precision
If manual segmentation of lung images is performed to accurately assess disease progression, then the measurement precision improves, but the productivity decreases due to time-consuming manual processes
Solution Approach 1:
The patent performs preliminary segmentation of lung images during the training phase to create labeled datasets. The machine learning models are pre-trained on these segmented images, enabling them to automatically perform accurate segmentation and disease progression assessment on new images without requiring manual segmentation at the time of assessment.
Solution Approach 2:
The patent replaces manual image segmentation with automated machine learning-based segmentation. The trained models automatically segment lung images and quantify disease progression, maintaining the measurement precision of manual segmentation while dramatically increasing productivity by eliminating the time-consuming manual process.
3Productivity
If machine learning models are trained on segmented lung images to automate disease progression assessment, then the productivity increases through automated analysis, but the device complexity increases due to model training and updating requirements
Solution Approach 1:
The patent develops universal machine learning models that can assess multiple types of lung diseases and progression stages. The trained models serve multiple functions including disease detection, progression assessment, and treatment response evaluation, reducing the need for separate specialized models for each clinical scenario.
Solution Approach 2:
The patent implements feedback mechanisms where model predictions are evaluated against ground truth data, and model performance is continuously monitored. This feedback loop enables iterative model improvement and validation, ensuring clinical accuracy while managing complexity through systematic model refinement.
4Reliability
If early detection of lung disease progression is achieved through advanced imaging analysis, then the reliability of patient diagnosis improves, but the difficulty of detecting and measuring increases due to subtle disease changes
Solution Approach 1:
The patent applies local quality analysis by focusing on specific regions and features within lung images that are most indicative of disease progression. The machine learning models identify and analyze subtle local changes in lung tissue characteristics, allowing early detection while managing the complexity of analyzing entire images for subtle changes.
Solution Approach 2:
The patent transforms subtle visual changes in lung images into quantifiable parameters through machine learning analysis. The models convert difficult-to-detect subtle disease progression into measurable numerical outputs, improving reliability by providing objective quantification of disease changes that would be difficult to detect through visual inspection alone.
Data Source
AI summary
A machine learning approach is herein provided for preparing a model for assessing the progression of a lung disease, comprises receiving a first set of segmented images of lungs from different patients with the lung disease. The first set of images is segmented and used to train the model. The trained model is applied to a set of unsegmented images to generate a second set of segmented images. The model is updated with the second set of segmented segmentation. From the model, at least one result associated with progression of the lung disease is outputted.