Neural Network Lung Slide Detection for Real-Time Pneumothorax Diagnosis
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Solution Overview
Problem
Current methods for diagnosing pneumothorax using lung ultrasound are time-consuming and prone to user error, limiting their use in real-time applications and impacting lifesaving efforts.
Innovation Solution
An automated detection system using neural networks to analyze B-mode and M-mode ultrasound images, determining lung sliding or non-sliding to aid in pneumothorax diagnosis, reducing operator variability and improving diagnostic accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinicians manually evaluate B-mode video clips and use M-mode to assess lung sliding, then diagnostic accuracy can be achieved, but the process is time-consuming and prone to user error
Solution Approach 1:
The patent replaces manual clinical evaluation with an automated neural network system that processes ultrasound images. The neural network automatically analyzes B-mode and M-mode images to detect lung sliding, eliminating the need for clinician expertise in manually interpreting these complex ultrasound patterns, thereby reducing time consumption while maintaining diagnostic accuracy
Solution Approach 2:
The system performs self-service by automatically generating diagnostic assessments without requiring skilled operators. The neural network independently evaluates lung sliding presence, generates probability scores, and provides diagnostic conclusions, making the system operable by users with minimal training while maintaining consistent diagnostic quality
2Reliability
If manual evaluation of lung sliding is performed by skilled clinicians, then diagnostic reliability is maintained, but operator variability and user error increase
Solution Approach 1:
The patent transforms the diagnostic process from subjective human interpretation to objective parameter-based analysis. The neural network processes quantitative image data and generates standardized probability scores for lung sliding detection, replacing variable human judgment with consistent algorithmic parameters that reduce operator variability and improve reliability
Solution Approach 2:
By substituting human operators with an automated neural network system, the patent eliminates operator variability and user error. The system provides consistent, reproducible diagnostic results across different users and conditions, maintaining high reliability without the skill-dependent variations inherent in manual evaluation
3Productivity
If automated neural network analysis is implemented, then diagnostic speed and real-time capability are improved, but system complexity increases
Solution Approach 1:
The patent integrates multiple diagnostic functions into a single unified neural network system that processes both B-mode and M-mode ultrasound images. This multi-functional approach enables real-time lung sliding detection, pneumothorax diagnosis, and probability scoring within one system, improving diagnostic speed while managing complexity through functional integration rather than multiple separate systems
Data Source
AI summary
Methods and apparatuses for performing automated detection of lung slide using a computing device (e.g., an ultrasound system, etc.) are disclosed. In some embodiments, the techniques determine lung sliding using one or more neural networks. In some embodiments, the neural networks are part of a process that determines probabilities of the lung sliding at one or more M-lines. In some embodiments, the techniques display one or more probabilities of lung sliding in a B-mode ultrasound image.