ArticleDiagnostics (Basel, Switzerland)2022
Quantitative Measurement of Pneumothorax Using Artificial Intelligence Management Model and Clinical Application.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- Artificial Intelligence in Pleural Diseases: Current Applications and Next Steps.Thoracic research and practice · 2026Article
- Development of an AI model for pneumothorax imaging: Dataset and model optimization strategies for real-world deployment.European journal of radiology open · 2025Article
- Factors for increasing positive predictive value of pneumothorax detection on chest radiographs using artificial intelligence.Scientific reports · 2024Article
- Prediction of gap balancing based on 2-D radiography in total knee arthroplasty for knee osteoarthritis patients.Arthroplasty (London, England) · 2023Article
- Watch Out for the Early Killers: Imaging Diagnosis of Thoracic Trauma.Korean journal of radiology · 2023Review
- Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?Diagnostics (Basel, Switzerland) · 2023Review
- Role of artificial intelligence in oncologic emergencies: a narrative review.Exploration of targeted anti-tumor therapy · 2023Review
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
Artificial intelligence (AI) techniques can be a solution for delayed or misdiagnosed pneumothorax. This study developed, a deep-learning-based AI model to estimate the pneumothorax amount on a chest radiograph and applied it to a treatment algorithm developed by experienced thoracic surgeons. U-net performed semantic segmentation and classification of pneumothorax and non-pneumothorax areas. The pneumothorax amount was measured using chest computed tomography (volume ratio, gold standard) and chest radiographs (area ratio, true label) and calculated using the AI model (area ratio, predicted label). Each value was compared and analyzed based on clinical outcomes. The study included 96 patients, of which 67 comprised the training set and the others the test set. The AI model showed an accuracy of 97.8%, sensitivity of 69.2%, a negative predictive value of 99.1%, and a dice similarity coefficient of 61.8%. In the test set, the average amount of pneumothorax was 15%, 16%, and 13% in the gold standard, predicted, and true labels, respectively. The predicted label was not significantly different from the gold standard (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.