Automated Lung-Slide Detection for Real-Time Pneumothorax Diagnosis
Find Innovative SolutionsGenerate Solutions
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 through a series of image processing steps to improve diagnostic accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated detection using neural networks is implemented, then diagnostic accuracy and speed are improved, but device complexity increases
Solution Approach 1:
The patent introduces an automated neural network system as an intermediary between the ultrasound imaging and diagnostic decision-making. This intermediary automatically detects lung sliding by analyzing B-mode and M-mode ultrasound images, eliminating the need for clinician interpretation while maintaining high diagnostic accuracy. The system processes images through trained neural networks that output diagnostic probabilities, serving as a mediator that bridges raw imaging data and clinical diagnosis.
2Reliability
If manual evaluation of B-mode video clips is performed, then diagnostic reliability is maintained, but time consumption increases
Solution Approach 1:
The patent replaces the mechanical process of manual visual evaluation with an automated computational system. Instead of clinicians manually examining B-mode video clips and M-mode images to detect lung sliding, the system uses neural networks to automatically analyze the same ultrasound data. This substitution maintains diagnostic reliability through trained algorithms while dramatically reducing the time required for evaluation, enabling real-time or near-real-time diagnosis.
3Reliability
If automated neural network detection is used, then operator variability is reduced, but ease of operation decreases
Solution Approach 1:
The patent implements a self-service automated detection system where the neural network independently performs the entire diagnostic evaluation without requiring operator skill or intervention. The system automatically processes ultrasound images, applies trained detection algorithms, and generates diagnostic outputs. This eliminates operator variability entirely as the same automated algorithm is applied consistently to all cases, though it requires the operator to simply acquire and present the ultrasound images for automatic analysis.
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.