ArticleJournal of the Korean Society of Radiology2024
[Diagnosis of Rib Fracture Using Artificial Intelligence on Chest CT Images of Patients with Chest Trauma].
Article in Journal of the Korean Society of Radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Chest Wall and Diaphragmatic Injuries: Imaging Evaluation and Clinical Considerations.Journal of the Korean Society of Radiology · 2026Review
- The role of AI assistance in the evaluation of unenhanced chest CT scans in an emergency setting.Polish journal of radiology · 2026Article
- "Could She/He Walk Out of the Hospital?": Implementing AI Models for Recovery Prediction and Doctor-Patient Communication in Major Trauma.Diagnostics (Basel, Switzerland) · 2025Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To determine the pros and cons of an artificial intelligence (AI) model developed to diagnose acute rib fractures in chest CT images of patients with chest trauma. Materials and Methods: A total of 1209 chest CT images (acute rib fracture [ Results: Sensitivity, specificity, positive and negative predictive values, and accuracy for diagnosing acute rib fractures in chest CT images were 93.3%, 94%, 97.9%, 82.5%, and 95.6% respectively. However, the accuracy of the location of acute rib fractures was low at 76% (760/1000). The cause of error in the diagnosis of acute rib fracture seemed to be a result of considering the scapula or clavicle that were in the same position (66%) or some ribs that were not recognized (34%). Conclusion: The AI model for diagnosing acute rib fractures showed high accuracy in detecting the presence of acute rib fractures, but diagnosis of the exact location of rib fractures was limited.
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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.