Predicting the Presence of Traumatic Chest Injuries Using Machine Learning Algorithm
Archives of Academic Emergency Medicine,
Vol. 13 No. 1 (2025),
6 September 2025
,
Page e41
https://doi.org/10.22037/aaemj.v13i1.2512
Abstract
Introduction: Various tools have been developed to determine the priority of radiography in trauma patients. This study aimed to investigate the role of machine learning models in predicting chest injuries following multiple trauma.
Methods: We used the database of a comprehensive cross-sectional survey conducted in 2015. Eight machine learning models were developed using demographic characteristics, physical exam findings, and radiologic results of 2860 patients.
Results: Area under the receiver operating characteristic curve (AUC) was greater than 0.96 in Random Forest, Gradient Boosting, XGBoost, Decision Tree, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbors (KNN), and Neural Network models. The random forest model, XGBoost and Gradient Boosting had the highest accuracy (0.99). Sensitivity was also highest in the Gradient Boosting, XGBoost and KNN models (0.99). The specificity of all of the models in predicting chest radiography outcomes of multiple trauma patients was higher than 0.97, except for logistic regression and SVM (0.912 and 0.885 respectively).
Conclusions: Our study highlights the strong potential of machine learning models, especially Random Forest and Gradient Boosting, in predicting chest trauma outcomes with high accuracy and sensitivity.
- Chest trauma
- Machine Learning
- Chest CT
- Chest X-ray
- Multiple trauma
- Thoracic injuries
- Machine learning algorithms
- Radiography, thoracic
- Detection algorithms
- Lung injury
How to Cite
References
Osifo OD, Iribhogbe PE, Ugiagbe EE. Epidemiology and pattern of paediatric and adolescent trauma deaths in a level 1 trauma centre in Benin city, Nigeria. Injury. 2012;43(11):1861-4.
Yadollahi M. A study of mortality risk factors among trauma referrals to trauma center, Shiraz, Iran, 2017. Chin J Traumatol. 2019;22(4):212-8.
Norman R, Matzopoulos R, Groenewald P, Bradshaw D. The high burden of injuries in South Africa. Bull World Health Organ. 2007;85(9):695-702.
Anghele M, Marina V, Anghele A-D, Moscu C-A, Dragomir L. Negative Factors Influencing Multiple-Trauma Patients. Clinics and Practice. 2024;14(4):1562-70.
Dennis BM, Bellister SA, Guillamondegui OD. Thoracic trauma. Surgical Clinics. 2017;97(5):1047-64.
Kool DR, Blickman JG. Advanced Trauma Life Support. ABCDE from a radiological point of view. Emerg Radiol. 2007;14(3):135-41.
Ong D, Cheung M, Cuenca P, Schauer S. Clinical Utility of Routine Chest X-Rays During the Initial Stabilization of Trauma Patients. South Med J. 2019;112(1):55-9.
Safari S, Yousefifard M, Baikpour M, Rahimi-Movaghar V, Abiri S, Falaki M, et al. Validation of thoracic injury rule out criteria as a decision instrument for screening of chest radiography in blunt thoracic trauma. J Clin Orthop Trauma. 2016;7(2):95-100.
Rodriguez RM, Anglin D, Langdorf MI, Baumann BM, Hendey GW, Bradley RN, et al. NEXUS chest: validation of a decision instrument for selective chest imaging in blunt trauma. JAMA Surg. 2013;148(10):940-6.
Safari S, Radfar F, Baratloo A. Thoracic injury rule out criteria and NEXUS chest in predicting the risk of traumatic intra-thoracic injuries: A diagnostic accuracy study. Injury. 2018;49(5):959-62.
Kulshrestha S, Dligach D, Joyce C, Gonzalez R, O'Rourke AP, Glazer JM, et al. Comparison and interpretability of machine learning models to predict severity of chest injury. JAMIA Open. 2021;4(1):ooab015.
Tan H, Xu H, Yu N, Yu Y, Duan H, Fan Q, et al. The value of deep learning-based computer aided diagnostic system in improving diagnostic performance of rib fractures in acute blunt trauma. BMC Med Imaging. 2023;23(1):55.
Gipson J, Tang V, Seah J, Kavnoudias H, Zia A, Lee R, et al. Diagnostic accuracy of a commercially available deep-learning algorithm in supine chest radiographs following trauma. Br J Radiol. 2022;95(1134):20210979.
Artificial Intelligence-enabled Automated Medical Prediction and Diagnosis in Trauma Patients. Dans: CRC Press eBooks; 2022. p. 135 45.
Lyu WH, Xia F, Zhou CS, Huang M, Ding WW, Zhang S, et al. [Application of deep learning-based chest CT auxiliary diagnosis system in emergency trauma patients]. Zhonghua Yi Xue Za Zhi. 2021;101(7):481-6. [Article in Chinese]
Wang S, Wu D, Ye L, Chen Z, Zhan Y, Li Y. Assessment of automatic rib fracture detection on chest CT using a deep learning algorithm. Eur Radiol. 2023 Mar;33(3):1824-1834. doi: 10.1007/s00330-022-09156-w. Epub 2022 Oct 10. PMID: 36214848.
Shahverdi Kondori M, Malek H. Determining the Need for Computed Tomography Scan Following Blunt Chest Trauma through Machine Learning Approaches. Arch Acad Emerg Med. 2021;9(1):e15.
Staziaki PV, Wu D, Rayan JC, Santo IDO, Nan F, Maybury A, et al. Machine learning combining CT findings and clinical parameters improves prediction of length of stay and ICU admission in torso trauma. Eur Radiol. 2021;31(7):5434-41.
Lee KC, Lin TC, Chiang HF, Horng GJ, Hsu CC, Wu NC, et al. Predicting outcomes after trauma: Prognostic model development based on admission features through machine learning. Medicine (Baltimore). 2021;100(49):e27753.
Shukla V, Pandey M, Sahu SK, Singh A, Khare A, Jeswani M, Bisen J, Lahariya CP. Clinicoepidemiological Study of Traumatic Chest Injuries in a Tertiary Care Center. Int J Sci Stud 2020;8(2):12-17.
Asgarzadeh S, Feizi B, Sarabandi F, Asgarzadeh M. Thoracic Injury Rule out Criteria in Prediction of Traumatic Intra-thoracic Injuries; a Validation Study. Emerg (Tehran). 2017;5(1):e27.
Yousefifard M, Hosseini M, Parvizi MR. Pediatric Thoracic Injury Rule out Criteria (pTIRC) in Diagnosis of Very Low Risk Children for Traumatic Intrathoracic Injuries; a Diagnostic Accuracy Study. Arch Acad Emerg Med. 2020;8(1):e7.
Vazirizadeh-Mahabadi M, Yarahmadi M. Canadian C-spine Rule versus NEXUS in Screening of Clinically Important Traumatic Cervical Spine Injuries; a systematic review and meta-analysis. Arch Acad Emerg Med. 2023;11(1):e5.
Peng HT, Siddiqui MM, Rhind SG, Zhang J, da Luz LT, Beckett A. Artificial intelligence and machine learning for hemorrhagic trauma care. Military Medical Research. 2023;10(1):6.
Zhang T, Nikouline A, Lightfoot D, Nolan B. Machine Learning in the Prediction of Trauma Outcomes: A Systematic Review. Annals of Emergency Medicine. 2022;80(5):440-55.
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