AI Strain Measurement for Sonoelastography
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Solution Overview
Problem
Conventional medical imaging systems, particularly in sonoelastography, face challenges in accurately assessing tissue softness and strain-ratio calculations, leading to potential over-diagnosis due to user-dependent visual examinations, which can result in unnecessary further examinations and increased healthcare costs.
Innovation Solution
The implementation of artificial intelligence (AI) algorithms and deep learning techniques within medical imaging systems to automatically measure strains and calculate strain-ratios, enhancing the reliability and accuracy of elastography examinations by analyzing ultrasound images and providing real-time feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual examination by radiologists is used to assess tissue softness and strain-ratio, then the examination can be performed with conventional equipment, but the measurement precision and reliability are insufficient leading to over-diagnosis
Solution Approach 1:
The patent replaces the mechanical/visual examination method with an AI-based automated measurement system. The AI algorithm automatically analyzes ultrasound images to measure strain values and calculate strain-ratios, eliminating the need for radiologist visual assessment and improving measurement precision and reliability.
Solution Approach 2:
The system enables self-service automated measurement where the AI algorithm independently performs strain analysis and strain-ratio calculation without requiring expert intervention. The automated system processes ultrasound images, determines tissue softness, and provides diagnostic feedback autonomously.
2Reliability
If automated AI-based measurement is implemented, then measurement precision and reliability improve, but device complexity and implementation cost increase
Solution Approach 1:
The patent replaces the mechanical/visual examination method with an AI-based automated measurement system. The AI algorithm automatically analyzes ultrasound images to measure strain values and calculate strain-ratios, eliminating the need for radiologist visual assessment and improving measurement precision and reliability.
Solution Approach 2:
The system provides automated feedback by calculating strain-ratios and comparing them against reference values to determine tissue softness and diagnostic outcomes. This feedback mechanism enhances diagnostic reliability by providing objective, quantifiable results that reduce ambiguity in tissue assessment.
3Ease of operation
If visual examination is used, then the system remains simple and cost-effective, but user-dependency increases leading to ambiguous assessments
Solution Approach 1:
The system enables self-service automated measurement where the AI algorithm independently performs strain analysis and strain-ratio calculation without requiring expert intervention. The automated system processes ultrasound images, determines tissue softness, and provides diagnostic feedback autonomously.
Solution Approach 2:
The system provides automated feedback by calculating strain-ratios and comparing them against reference values to determine tissue softness and diagnostic outcomes. This feedback mechanism enhances diagnostic reliability by providing objective, quantifiable results that reduce ambiguity in tissue assessment.
Data Source
AI summary
Systems and methods are provided for automatic measurement of strains and strain-ratio calculation for sonoelastography.