Machine-Learned Composite Dictionaries for Lung Tissue Assessment
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Solution Overview
Problem
Current methods for lung tissue assessment, particularly in extended criteria donors, face challenges such as time-consuming manual segmentation and classification of air trapping in CT images, prone to errors due to background clutter and subjective bias, limiting the expansion of the donor pool for lung transplants.
Innovation Solution
A computer-implemented method using machine-learned composite dictionaries, where sparse codes are determined for input data samples relative to dictionaries learned from training data, optimizing dictionary bases to minimize intra-class differences and maximize inter-class differences, facilitating efficient tissue assessment and air trapping classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation and classification of air trapping in CT images is performed, then diagnostic accuracy can be maintained, but the time required for processing increases significantly and subjective bias introduces errors
Solution Approach 1:
The patent replaces manual segmentation and classification methods with an automated machine learning system. The system uses pre-trained models that automatically detect air trapping regions in CT images, eliminating the need for radiologists to manually segment and classify each region while maintaining diagnostic accuracy and reducing processing time.
Solution Approach 2:
The patent creates a digital copy of the manual segmentation process through automated algorithms. The system learns from annotated training data and reproduces the segmentation and classification tasks automatically, preserving the diagnostic precision of manual methods while eliminating time consumption and subjective bias.
2Measurement precision
If manual segmentation and classification is performed, then diagnostic accuracy can be maintained, but errors increase due to background clutter, non-uniform illumination, imaging noise, and subjective bias
Solution Approach 1:
The patent replaces manual segmentation with automated machine learning algorithms that objectively analyze CT images. The system processes images without human intervention, eliminating subjective bias and reducing errors caused by background clutter, non-uniform illumination, and imaging noise through consistent, repeatable computational analysis.
Solution Approach 2:
The system performs self-calibration and automatic adaptation to handle variations in imaging conditions. The machine learning models are trained to recognize patterns across different imaging conditions, enabling them to maintain high diagnostic accuracy without external intervention or manual adjustment.
3Productivity
If computerized frameworks for tissue classification are used, then the workload on radiologists is reduced, but the complexity of the system increases
Solution Approach 1:
The patent employs pre-trained machine learning models that are prepared in advance with extensive training data. This preliminary action allows the system to perform complex classification tasks independently without requiring radiologists to manually segment or analyze each image, significantly reducing workload while the system handles its own complexity through automated processing.
4Quantity of substance
If extended criteria donors are used to expand the donor pool, then the number of available lungs increases, but the difficulty of screening and classifying their lung tissue increases
Solution Approach 1:
The patent replaces manual screening methods with automated machine learning classification systems specifically trained to assess extended criteria donors. The system automatically evaluates lung tissue characteristics in CT images, making the screening process faster and more reliable while expanding the donor pool by objectively assessing previously unsuitable candidates.
Data Source
AI summary
A computer-implemented method of tissue assessment includes obtaining a plurality of input data samples, each input data sample being representative of tissue in one of multiple views, determining a set of sparse codes for each input data sample of the plurality of input data samples, determining a reconstruction error for the set of sparse codes relative to each dictionary of a set of machine-learned composite dictionaries, and providing the tissue assessment in accordance with the machine-learned composite dictionary in the set of machine-learned composite dictionaries having a minimum reconstruction error of the determined reconstruction errors. Each machine-learned composite dictionary includes a plurality of constituent dictionaries, each constituent dictionary of the plurality of constituent dictionaries being associated with a respective one of the multiple views.