Development and evaluation of a deep learning-based model for simultaneous detection and localization of rib and clavicle fractures in trauma patients' chest radiographs

Chi-Tung Cheng; Ling-Wei Kuo; Chun-Hsiang Ouyang; Chi-Po Hsu; Wei-Cheng Lin; Chih-Yuan Fu; Shih-Ching Kang; Chien-Hung Liao

doi:10.1136/tsaco-2023-001300

Development and evaluation of a deep learning-based model for simultaneous detection and localization of rib and clavicle fractures in trauma patients' chest radiographs

Trauma Surg Acute Care Open. 2024 Apr 12;9(1):e001300. doi: 10.1136/tsaco-2023-001300. eCollection 2024.

Authors

Chi-Tung Cheng^{1

2}, Ling-Wei Kuo^{1

2}, Chun-Hsiang Ouyang^{1

2}, Chi-Po Hsu^{1

2}, Wei-Cheng Lin³, Chih-Yuan Fu^{1

2}, Shih-Ching Kang^{1

2}, Chien-Hung Liao^{1

2}

Affiliations

¹ Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan.
² Department of medicine, Chang Gung university, Taoyuan, Taiwan.
³ Department of Electrical Engineering, Chang Gung University, Taoyuan, Taiwan.

Abstract

Purpose: To develop a rib and clavicle fracture detection model for chest radiographs in trauma patients using a deep learning (DL) algorithm.

Materials and methods: We retrospectively collected 56 145 chest X-rays (CXRs) from trauma patients in a trauma center between August 2008 and December 2016. A rib/clavicle fracture detection DL algorithm was trained using this data set with 991 (1.8%) images labeled by experts with fracture site locations. The algorithm was tested on independently collected 300 CXRs in 2017. An external test set was also collected from hospitalized trauma patients in a regional hospital for evaluation. The receiver operating characteristic curve with area under the curve (AUC), accuracy, sensitivity, specificity, precision, and negative predictive value of the model on each test set was evaluated. The prediction probability on the images was visualized as heatmaps.

Results: The trained DL model achieved an AUC of 0.912 (95% CI 87.8 to 94.7) on the independent test set. The accuracy, sensitivity, and specificity on the given cut-off value are 83.7, 86.8, and 80.4, respectively. On the external test set, the model had a sensitivity of 88.0 and an accuracy of 72.5. While the model exhibited a slight decrease in accuracy on the external test set, it maintained its sensitivity in detecting fractures.

Conclusion: The algorithm detects rib and clavicle fractures concomitantly in the CXR of trauma patients with high accuracy in locating lesions through heatmap visualization.

Keywords: Algorithms; rib fractures.