Artificial Intelligence-Assisted Lung Ultrasound for Pneumothorax: Diagnostic Accuracy Compared with CT in Emergency and Critical Care
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View abstract on PubMed
Summary
This summary is machine-generated.Artificial intelligence (AI) significantly enhances lung ultrasound (LUS) for detecting pneumothorax (PTX), achieving near-expert accuracy. This AI-assisted approach offers a faster, more reliable diagnostic tool for critical care settings.
Area Of Science
- Medical Imaging
- Artificial Intelligence
- Critical Care Medicine
Background
- Pneumothorax (PTX) diagnosis is critical in emergency and critical care.
- Lung ultrasound (LUS) is a rapid, radiation-free alternative to CT, but operator-dependent.
- Artificial intelligence (AI) can potentially standardize LUS interpretation and improve accuracy.
Purpose Of The Study
- To evaluate the performance of AI, specifically transformer models, in detecting pneumothorax using lung ultrasound.
- To compare the accuracy of different AI models (ViT, DINOv2, RF, XGBoost) and imaging modes (B-mode, M-mode) for PTX detection.
Main Methods
- Retrospective analysis of 46 patients (23 with PTX, 23 controls) using CT as reference.
- Extraction of 2760 B-mode and M-mode ultrasound frames.
- Training and testing of transformer models (ViT, DINOv2) and classifiers (RF, XGBoost) on image data and video features.
Main Results
- Transformer models achieved high diagnostic accuracy, with B-mode images generally outperforming M-mode.
- DINOv2 demonstrated strong performance in B-mode (90% accuracy, 80% sensitivity, 100% specificity) in a patient-level split scenario.
- AI models, including RF and XGBoost on video features, reached up to 90% accuracy in PTX detection.
Conclusions
- AI-assisted LUS substantially improves pneumothorax detection, with transformer models like DINOv2 showing near-expert accuracy.
- Further validation on larger, multicenter datasets is necessary for clinical integration of AI in LUS for PTX diagnosis.
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