[Diagnosis of Rib Fracture Using Artificial Intelligence on Chest CT Images of Patients with Chest Trauma].

외상 환자의 흉부 CT에서 인공지능을 이용한 갈비뼈 골절 진단.

Publisher: Korean Society of Radiology Country of Publication: Korea (South) NLM ID: 9918452587406676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2951-0805 (Electronic) Linking ISSN: 29510805 NLM ISO Abbreviation: J Korean Soc Radiol Subsets: PubMed not MEDLINE

Original Publication: Seoul : Korean Society of Radiology, [2021]-

Competing Interests: Conflicts of Interest: The authors have no potential conflicts of interest to disclose.
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 [ n = 1159], normal [ n = 50]) were selected among patients with chest trauma. Among 1159 acute rib fracture CT images, 9 were randomly selected for AI model training. 150 acute rib fracture CT images and 50 normal ones were tested, and the remaining 1000 acute rib fracture CT images was internally verified. We investigated the diagnostic accuracy and errors of AI model for the presence and location of acute rib fractures.
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.
(Copyrights © 2024 The Korean Society of Radiology.)

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Date Created: 20240812 Latest Revision: 20240813

20250114

PMC11310438

10.3348/jksr.2023.0099

39130793