Automated Lung Sliding 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 a computing device with neural networks to analyze B-mode and M-mode ultrasound images, determining lung sliding probabilities and displaying them in real-time, reducing operator variability and improving diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinicians manually evaluate B-mode video clips and use M-mode to detect 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 mechanical evaluation by clinicians with an automated computer vision system. The system uses deep learning models (U-Net for segmentation, ResNet for classification) to automatically detect pleural lines, track lung movement, and diagnose pneumothorax, eliminating the need for manual frame-by-frame analysis while maintaining diagnostic accuracy.
Solution Approach 2:
The system performs self-service by automatically processing ultrasound images without requiring skilled operator intervention. The automated pipeline includes quality assessment, segmentation, tracking, and diagnosis all performed by the system itself, making the diagnostic process independent of operator expertise and reducing time consumption.
2Measurement precision
If clinicians manually evaluate lung sliding, then diagnostic accuracy can be achieved, but user error and operator variability occur
Solution Approach 1:
The patent replaces manual mechanical evaluation by clinicians with an automated computer vision system. The system uses deep learning models (U-Net for segmentation, ResNet for classification) to automatically detect pleural lines, track lung movement, and diagnose pneumothorax, eliminating the need for manual frame-by-frame analysis while maintaining diagnostic accuracy.
Solution Approach 2:
The system incorporates quality assessment feedback to evaluate the suitability of ultrasound images for analysis. By continuously monitoring image quality metrics and providing feedback on whether images meet diagnostic criteria, the system ensures reliable processing while maintaining high diagnostic accuracy and reducing errors from poor-quality images.
3Productivity
If automated detection systems are implemented, then speed and accuracy improve, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex diagnostic task into separate modular components: quality assessment module, pleural line detection module, lung movement tracking module, and diagnosis module. Each module is handled by specialized deep learning models (U-Net, ResNet), making the overall complex system manageable and easier to implement while achieving high diagnostic speed and accuracy.
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.