Current Review of Analgesia and Regional Anesthesia Practices for Rib Fracture and Chest Wall Trauma

Piennette, Paul D.; Morris, Sara; Northern, Theresa

doi:10.1007/s40140-022-00530-4

Cited by 1 publication

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rib fractures are associated with significant morbidity and are detected in at least 10% of all injured patients [ 1 ], with patients frequently requiring admission to the intensive care unit (ICU) and mortality rates as high as 33% [ 2 ]. To avoid serious consequences, accurate detection of rib fractures is important.…”

Section: Introductionmentioning

confidence: 99%

Rib Fracture Detection with Dual-Attention Enhanced U-Net

Zhou

Jia

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Rib fractures are common injuries caused by chest trauma, which may cause serious consequences. It is essential to diagnose rib fractures accurately. Low-dose thoracic computed tomography (CT) is commonly used for rib fracture diagnosis, and convolutional neural network- (CNN-) based methods have assisted doctors in rib fracture diagnosis in recent years. However, due to the lack of rib fracture data and the irregular, various shape of rib fractures, it is difficult for CNN-based methods to extract rib fracture features. As a result, they cannot achieve satisfying results in terms of accuracy and sensitivity in detecting rib fractures. Inspired by the attention mechanism, we proposed the CFSG U-Net for rib fracture detection. The CSFG U-Net uses the U-Net architecture and is enhanced by a dual-attention module, including a channel-wise fusion attention module (CFAM) and a spatial-wise group attention module (SGAM). CFAM uses the channel attention mechanism to reweight the feature map along the channel dimension and refine the U-Net’s skip connections. SGAM uses the group technique to generate spatial attention to adjust feature maps in the spatial dimension, which allows the spatial attention module to capture more fine-grained semantic information. To evaluate the effectiveness of our proposed methods, we established a rib fracture dataset in our research. The experimental results on our dataset show that the maximum sensitivity of our proposed method is 89.58%, and the average FROC score is 81.28%, which outperforms the existing rib fracture detection methods and attention modules.

show abstract

Section: Introductionmentioning

confidence: 99%