Ultrasound Pattern Recognition for Trauma Detection
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Solution Overview
Problem
Current systems for automatic focused assessment with sonography for trauma (FAST) exams face challenges in accurately and efficiently identifying internal trauma, such as pneumothorax and hemothorax, especially in field settings where expertise and equipment limitations exist.
Innovation Solution
A method and system that filters ultrasonic images to remove artifacts, identifies specific patterns like A-line, B-line, lung sliding, and barcode patterns, and applies rules to diagnose internal trauma using a processor and analysis engine, integrated into a portable, user-friendly device for rapid and accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automatic pattern recognition algorithms are implemented to improve diagnostic accuracy, then the system complexity increases
Solution Approach 1:
The diagnostic system segments the complex task of trauma detection into distinct pattern recognition modules, each specializing in identifying specific ultrasound patterns (e.g., lung sliding, barcode, seashore patterns). This modular approach improves diagnostic accuracy for different trauma types while managing system complexity through organized, reusable components.
Solution Approach 2:
The system introduces an intermediate layer of automated pattern recognition algorithms that act as mediators between the raw ultrasound images and the final diagnostic conclusions. This intermediary processing layer enhances measurement precision by systematically analyzing patterns that may be difficult for human operators to detect consistently.
2Ease of operation
If automated analysis is used to reduce training time for novice users, then the device complexity increases
Solution Approach 1:
The system implements self-service functionality by automatically analyzing ultrasound images and providing diagnostic assistance without requiring extensive user training. The automated pattern recognition algorithms perform the complex analysis tasks that would otherwise require highly trained operators, thereby reducing training time while the system handles its own analytical requirements.
Solution Approach 2:
The system replaces the mechanical aspect of human expert analysis with automated computational algorithms. Instead of relying on the mechanical skill and experience of trained operators, the system uses computer-based pattern recognition to perform the same diagnostic function, reducing the need for extensive training while managing device complexity through software implementation.
3Measurement precision
If multiple pattern recognition algorithms are implemented to detect various trauma types, then the processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-programming multiple pattern recognition algorithms that can simultaneously or sequentially analyze different trauma patterns. This preliminary preparation of analytical tools allows the system to quickly match observed ultrasound patterns against known trauma signatures, improving detection accuracy without excessive processing time during actual use.
Solution Approach 2:
The system applies partial action by implementing a hierarchy of pattern recognition algorithms, where more critical or common trauma patterns are analyzed with higher priority and computational resources. Not all patterns require equal processing intensity, allowing the system to maintain high detection accuracy for critical conditions while managing overall processing speed through selective analysis depth.
Data Source
AI summary
An embodiment of the invention provides a method for identifying internal trauma in a patient for pneumothorax, hemothorax and abdominal hemorrhage using ultrasound in B-modes with radial, longitudinal, phased array probes, and with M-mode for verification of lung sliding and lung point. Identifications are based on statistical classifications of image features, including A-line, B-line, lung sliding, barcode, sky, seashore, and beach patterns. For blood pools, a polygon is fitted to the boundary, and a cellular automaton extracts local interference patterns due to cavity shape. Logic is then applied to extractions to identify the trauma. With B-mode, feature extraction involves specialized algorithms operating at frame rate for tracking of features such as ribs and rib shadows, pleural line and fast changes in peak intensities along the pleural line. Results are presented on screen by means of highlighting and textual cues.