Deep Learning Framework for Ultrasound Lesion Classification
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Solution Overview
Problem
Current ultrasound imaging modalities, such as B-mode and color Doppler imaging, lack specificity and sensitivity in characterizing and classifying focal liver lesions, requiring expert knowledge and complex analysis, especially in differentiating between benign and malignant lesions.
Innovation Solution
A deep learning framework is applied to contrast-enhanced ultrasound (CEUS) images using a 3D predictive network to classify lesions as malignant or benign, and further into subclasses, by analyzing spatiotemporal characteristics of microbubble dynamics and blood-flow enhancement patterns across different phases, generating a text report and identifying decisive frames for interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contrast-enhanced ultrasound (CEUS) imaging is used to characterize focal liver lesions, then diagnostic specificity and sensitivity are improved, but analysis complexity and requirement for expert knowledge increase
Solution Approach 1:
The system employs automated deep learning algorithms that autonomously perform lesion characterization without requiring expert manual analysis. The neural network automatically processes CEUS image sequences, extracts spatiotemporal features, and generates diagnostic classifications, enabling the system to serve itself rather than requiring constant expert intervention for complex analysis.
Solution Approach 2:
The patent replaces the mechanical process of manual expert analysis with an automated computational system. Instead of relying on radiologists to manually interpret complex CEUS patterns, the system uses deep learning models that automatically detect and classify lesion characteristics based on trained patterns from extensive datasets.
2Reliability
If manual expert analysis is performed for lesion classification, then diagnostic accuracy is maintained, but time consumption and operational complexity increase
Solution Approach 1:
The deep learning system performs autonomous diagnostic analysis, eliminating the time-consuming process of manual expert review. The automated algorithm continuously processes CEUS sequences and provides real-time classification results without requiring radiologists to manually examine each frame or pattern.
Solution Approach 2:
The system performs preliminary computational analysis by pre-processing CEUS images, extracting features, and preparing data for classification before final diagnostic output is generated. This preliminary automated action reduces the time experts would otherwise need to spend on manual feature extraction and pattern recognition.
3Ease of operation
If deep learning framework is applied to automate lesion assessment, then operational simplicity and consistency are improved, but computational requirements and system complexity increase
Solution Approach 1:
The deep learning system is designed to operate autonomously, taking CEUS image sequences as input and automatically generating diagnostic classifications without requiring user intervention for complex parameter adjustment or manual feature selection. This self-service capability provides operational simplicity despite the underlying computational complexity.
Solution Approach 2:
The patent introduces an intermediary layer of automated computational processing between the CEUS imaging system and the final diagnostic output. This intermediary deep learning framework handles the complexity of pattern recognition and classification, shielding users from computational intricacies while providing simplified operational interfaces.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The deep learning framework automates lesion assessment, providing consistent and accurate classification and characterization of liver lesions, assisting clinicians with probability distributions and text reports, and reducing reliance on expert interpretation.
Implementation Method 1
color Doppler imaging, and/or power Doppler imaging are commonly used to characterize soft tissues, retrieve vasculature information, and quantify blood flow
Implementation Method 2
stabilized gas-filled microbubbles are used as contrast agents to increase the detectability of blood flow under ultrasound imaging
Data Source
AI summary
Clinical assessment devices, systems, and methods are provided. A clinical assessment system, comprising a processor in communication with an imaging device, wherein the processor is configured to receive, from the imaging device, a sequence of image frames representative of a contrast agent perfused subjects tissue across a time period; classify the sequence of image frames into a plurality of first tissue classes and a plurality of second tissue classes based on a spatiotemporal correlation among the sequence of image frames by applying a predictive network to the sequence of image frames to produce a probability distribution for the plurality of first tissue classes and the plurality of second tissue classes; and output, to a display in communication with the processor, the probability distribution for the plurality of first tissue classes and the plurality of second tissue classes.