Application of deep learning algorithm to detect and visualize vertebral fractures on plain frontal radiographs

Hsuan Yu Chen, Benny Wei Yun Hsu, Yu Kai Yin, Feng Huei Lin, Tsung Han Yang, Rong Sen Yang, Chih Kuo Lee, Vincent Shin-Mu Tseng*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background Identification of vertebral fractures (VFs) is critical for effective secondary fracture prevention owing to their association with the increasing risks of future fractures. Plain abdominal frontal radiographs (PARs) are a common investigation method performed for a variety of clinical indications and provide an ideal platform for the opportunistic identification of VF. This study uses a deep convolutional neural network (DCNN) to identify the feasibility for the screening, detection, and localization of VFs using PARs. Methods A DCNN was pretrained using ImageNet and retrained with 1306 images from the PARs database obtained between August 2015 and December 2018. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were evaluated. The visualization algorithm gradient-weighted class activation mapping (Grad-CAM) was used for model interpretation. Results Only 46.6% (204/438) of the VFs were diagnosed in the original PARs reports. The algorithm achieved 73.59% accuracy, 73.81% sensitivity, 73.02% specificity, and an AUC of 0.72 in the VF identification. Conclusion Computer driven solutions integrated with the DCNN have the potential to identify VFs with good accuracy when used opportunistically on PARs taken for a variety of clinical purposes. The proposed model can help clinicians become more efficient and economical in the current clinical pathway of fragile fracture treatment.

Original languageEnglish
Article numbere0245992
JournalPLoS ONE
Volume16
Issue number1 January
DOIs
StatePublished - Jan 2021

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