Deep convolutional neural network for rib fracture recognition on chest radiographs

Shu-Tien Huang; Liong-Rung Liu; Hung-Wen Chiu; Ming-Yuan Huang; Ming-Feng Tsai

doi:10.3389/fmed.2023.1178798

Deep convolutional neural network for rib fracture recognition on chest radiographs

Front Med (Lausanne). 2023 Aug 1:10:1178798. doi: 10.3389/fmed.2023.1178798. eCollection 2023.

Authors

Shu-Tien Huang^{1

2

3}, Liong-Rung Liu^{1

2

3}, Hung-Wen Chiu^{3

4}, Ming-Yuan Huang^{1

2}, Ming-Feng Tsai^{2

3

5}

Affiliations

¹ Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan.
² Department of Medicine, Mackay Medical College, New Taipei City, Taiwan.
³ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
⁴ Big Data Research Center, College of Management, Taipei Medical University, Taipei, Taiwan.
⁵ Division of Plastic Surgery, Department of Surgery, Mackay Memorial Hospital, Taipei, Taiwan.

Abstract

Introduction: Rib fractures are a prevalent injury among trauma patients, and accurate and timely diagnosis is crucial to mitigate associated risks. Unfortunately, missed rib fractures are common, leading to heightened morbidity and mortality rates. While more sensitive imaging modalities exist, their practicality is limited due to cost and radiation exposure. Point of care ultrasound offers an alternative but has drawbacks in terms of procedural time and operator expertise. Therefore, this study aims to explore the potential of deep convolutional neural networks (DCNNs) in identifying rib fractures on chest radiographs.

Methods: We assembled a comprehensive retrospective dataset of chest radiographs with formal image reports documenting rib fractures from a single medical center over the last five years. The DCNN models were trained using 2000 region-of-interest (ROI) slices for each category, which included fractured ribs, non-fractured ribs, and background regions. To optimize training of the deep learning models (DLMs), the images were segmented into pixel dimensions of 128 × 128.

Results: The trained DCNN models demonstrated remarkable validation accuracies. Specifically, AlexNet achieved 92.6%, GoogLeNet achieved 92.2%, EfficientNetb3 achieved 92.3%, DenseNet201 achieved 92.4%, and MobileNetV2 achieved 91.2%.

Discussion: By integrating DCNN models capable of rib fracture recognition into clinical decision support systems, the incidence of missed rib fracture diagnoses can be significantly reduced, resulting in tangible decreases in morbidity and mortality rates among trauma patients. This innovative approach holds the potential to revolutionize the diagnosis and treatment of chest trauma, ultimately leading to improved clinical outcomes for individuals affected by these injuries. The utilization of DCNNs in rib fracture detection on chest radiographs addresses the limitations of other imaging modalities, offering a promising and practical solution to improve patient care and management.

Keywords: artificial intelligence (AI); chest radiographs; deep convolutional neural network (DCNN); deep learning model (DLM); rib fracture; transfer learning.