Acute medicine & surgery · 2026
Impact of Artificial Intelligence Assistance on Diagnosing Traumatic Pneumothorax: A Comparison of Specialist and Non-Specialist Emergency Physicians.
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Suzuki Keisuke, Hiruma Kaede, Miyamoto Kazuyuki, Saeki Miho, Kanai Takahiro, Tomita Yoshitaka, Kikuchi Kazuki, Inoue Gen, Kaki Yuki, Yagi Masaharu, Sasaki Jun, Dohi Kenji
Аннотация
INTRODUCTION: Supine chest radiography is routinely used in trauma care; however, its sensitivity is limited in pneumothorax detection. Although artificial intelligence (AI) has recently been introduced as a diagnostic support tool for chest radiograph interpretation, its use in trauma radiographs and its interaction with physician experience remain unclear. We aimed to evaluate the diagnostic performance of AI-assisted image interpretation in traumatic pneumothorax detection among emergency medicine specialists and non-specialists.
METHODS: In this retrospective single-center study, 34 supine chest radiographs (17 pneumothorax and 17 non-pneumothorax cases confirmed with computed tomography) were interpreted by 20 emergency medicine physicians (10 specialists and 10 non-specialists). Each participant reviewed all radiographs twice: first without AI assistance and then with AI assistance after a 2-week washout period. Sensitivity, specificity, accuracy, and precision were calculated. To account for clustering of observations within readers and cases, mixed-effects logistic regression analysis was performed.
RESULTS: AI assistance significantly improved sensitivity and diagnostic accuracy across participants, whereas specificity and precision were not significantly affected. Specialists demonstrated higher baseline sensitivity and accuracy than non-specialists. In the mixed-effects logistic regression analysis, AI assistance was independently associated with improved diagnostic accuracy (odds ratio 2.37, p < 0.001). Additionally, the interaction between AI assistance and physician specialist status was significant (p = 0.010).
CONCLUSION: AI-assisted interpretation of supine chest radiographs is a potentially useful decision-support tool for detecting traumatic pneumothorax in emergency settings.
METHODS: In this retrospective single-center study, 34 supine chest radiographs (17 pneumothorax and 17 non-pneumothorax cases confirmed with computed tomography) were interpreted by 20 emergency medicine physicians (10 specialists and 10 non-specialists). Each participant reviewed all radiographs twice: first without AI assistance and then with AI assistance after a 2-week washout period. Sensitivity, specificity, accuracy, and precision were calculated. To account for clustering of observations within readers and cases, mixed-effects logistic regression analysis was performed.
RESULTS: AI assistance significantly improved sensitivity and diagnostic accuracy across participants, whereas specificity and precision were not significantly affected. Specialists demonstrated higher baseline sensitivity and accuracy than non-specialists. In the mixed-effects logistic regression analysis, AI assistance was independently associated with improved diagnostic accuracy (odds ratio 2.37, p < 0.001). Additionally, the interaction between AI assistance and physician specialist status was significant (p = 0.010).
CONCLUSION: AI-assisted interpretation of supine chest radiographs is a potentially useful decision-support tool for detecting traumatic pneumothorax in emergency settings.