Anatomy-Directed Ultrasound for Accurate Free Fluid Detection
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Solution Overview
Problem
Conventional ultrasound systems often fail to accurately detect the presence of free fluid in patients due to misinterpretation of fatty tissue as fluid, leading to false positives or underestimation/overestimation of fluid amounts, which can result in inadequate or unnecessary medical interventions.
Innovation Solution
An anatomy-directed ultrasound system that uses machine-learned models to identify bodily structures and generate focused ultrasound data by refining the scan based on organ positions, adjusting imaging parameters, and generating weight maps to enhance the detection of items of interest such as free fluid.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional ultrasound systems are used to detect free fluid, then the examination can be performed rapidly, but the accuracy of detection deteriorates due to misinterpretation of fatty tissue as fluid
Solution Approach 1:
An AI-based image processing system acts as an intermediary between the ultrasound scanner and the operator. The system automatically analyzes ultrasound images, identifies anatomical structures, and highlights regions containing free fluid, thereby improving detection accuracy without compromising the rapid examination workflow
Solution Approach 2:
The system replaces manual visual interpretation by the operator with an automated AI-based detection system. The AI model processes ultrasound images and provides precise identification of free fluid, eliminating the mechanical limitation of human interpretation while maintaining the rapid examination pace
2Device complexity
If the ultrasound operator manually interprets the ultrasound images, then the system remains simple, but false positives occur when misidentifying fatty tissue as free fluid
Solution Approach 1:
The ultrasound system performs self-service through automated AI-based analysis. The system independently processes ultrasound images, identifies anatomical structures, and detects free fluid without requiring complex manual intervention, thereby maintaining simplicity while reducing false positives
Solution Approach 2:
The AI system provides feedback to the operator by highlighting detected free fluid regions and anatomical structures. This feedback mechanism allows the operator to verify findings and correct potential misinterpretations, improving reliability while keeping the overall system simple
3Productivity
If the operator scans quickly to improve productivity, then the examination time is reduced, but the ability to detect all free fluid deteriorates
Solution Approach 1:
The AI system performs preliminary analysis of the entire ultrasound image before the operator views it. It automatically identifies all potential free fluid regions and highlights them, ensuring complete detection even when the operator scans quickly, thus maintaining both productivity and measurement precision
4Measurement precision
If the operator focuses attention on specific regions, then the detection accuracy in those regions improves, but the overall coverage of the examination deteriorates
Solution Approach 1:
The AI system segments the ultrasound image into different anatomical regions and automatically analyzes each segment for free fluid. This segmentation allows the system to provide precise detection in each region while maintaining overall coverage, as every segment is processed independently and comprehensively
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
Improves the accuracy of detecting and classifying free fluid by reducing false positives and ensuring appropriate medical interventions, while also being applicable to various bodily structures and conditions.
Implementation Method 1
Ultrasound systems do so by transmitting sound waves at frequencies above the audible spectrum into a body, receiving echo signals caused by the sound waves reflecting from internal body parts
Data Source
AI summary
Systems and methods for anatomy-directed ultrasound are described. In some implementations, an anatomy-directed ultrasound system generates ultrasound data from an ultrasound scan of an anatomy, which is a bodily structure of an organism (e.g., human or animal). The system identifies organs represented in the ultrasound data and information associated with the organs including position and type of organ. Using this information, the system obtains or generates new ultrasound data that includes a region in which an item of interest is likely to be located. For example, the system can crop the original ultrasound data, refocus the ultrasound scan (e.g., by adjusting imaging parameters) to image the region that is likely to include the item of interest, or generate a weight map indicating the region. The anatomy-directed ultrasound system can increase accuracy and reduce the number of false positives in comparison to the number detected by conventional ultrasound systems.