AUTHOR=Qiang Xiaoyong , Wang Qiang , Liu Guanjun , Song Limei , Zhou Weibin , Yu Ming , Wu Hang 

TITLE=Use video comprehension technology to diagnose ultrasound pneumothorax like a doctor would

JOURNAL=Frontiers in Physiology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1530808

DOI=10.3389/fphys.2025.1530808

ISSN=1664-042X

ABSTRACT=IntroductionEmergency rescue scenes and pre-hospital emergency stages commonly encounter trauma victims. Life-saving measures must be taken at the scene if a trauma patient has pneumothorax; if the patient is not evaluated and diagnosed right away, their life may be in jeopardy. Ultrasound, which has the advantages of being non-invasive, non-radioactive, portable, rapid, and repeatable, can be used to diagnose pneumothorax. However, those who interpret ultrasound images must undergo extensive, specialized, and rigorous training. Deep learning technology allows for the intelligent diagnosis of ultrasound images, allowing general healthcare professionals to quickly and with minimal training diagnose pneumothorax in lung ultrasound patients.MethodsPrevious studies focused primarily on the lung-sliding characteristics of M-mode images, neglecting other key features in lung ultrasonography pneumothorax, and used similar technological techniques. Our study team used video understanding technology for medical ultrasound imaging diagnostics, training the TSM video understanding model on the ResNet50 network with 657 clips and testing the model with untrained 164 lung ultrasound clips.ResultsThe model’s sensitivity was 99.21%, specificity was 89.19%, and average accuracy was 96.95%. The F1 score was 0.929, and the AUC was 0.97.DiscussionThis study is the first to apply video understanding models to the multi-feature fusion diagnosis of pneumothorax, demonstrating the feasibility of using video understanding technology in medical image diagnosis.