Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs

Chi Tung Cheng, Tsung Ying Ho, Tao Yi Lee, Chih Chen Chang, Ching Cheng Chou, Chih Chi Chen, I. Fang Chung, Chien Hung Liao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

113 Scopus citations

Abstract

Objective: To identify the feasibility of using a deep convolutional neural network (DCNN) for the detection and localization of hip fractures on plain frontal pelvic radiographs (PXRs). Summary of background data: Hip fracture is a leading worldwide health problem for the elderly. A missed diagnosis of hip fracture on radiography leads to a dismal prognosis. The application of a DCNN to PXRs can potentially improve the accuracy and efficiency of hip fracture diagnosis. Methods: A DCNN was pretrained using 25,505 limb radiographs between January 2012 and December 2017. It was retrained using 3605 PXRs between August 2008 and December 2016. The accuracy, sensitivity, false-negative rate, and area under the receiver operating characteristic curve (AUC) were evaluated on 100 independent PXRs acquired during 2017. The authors also used the visualization algorithm gradient-weighted class activation mapping (Grad-CAM) to confirm the validity of the model. Results: The algorithm achieved an accuracy of 91%, a sensitivity of 98%, a false-negative rate of 2%, and an AUC of 0.98 for identifying hip fractures. The visualization algorithm showed an accuracy of 95.9% for lesion identification. Conclusions: A DCNN not only detected hip fractures on PXRs with a low false-negative rate but also had high accuracy for localizing fracture lesions. The DCNN might be an efficient and economical model to help clinicians make a diagnosis without interrupting the current clinical pathway. Key Points: • Automated detection of hip fractures on frontal pelvic radiographs may facilitate emergent screening and evaluation efforts for primary physicians. • Good visualization of the fracture site by Grad-CAM enables the rapid integration of this tool into the current medical system. • The feasibility and efficiency of utilizing a deep neural network have been confirmed for the screening of hip fractures.

Original languageEnglish
Pages (from-to)5469-5477
Number of pages9
JournalEuropean Radiology
Volume29
Issue number10
DOIs
StatePublished - 1 Oct 2019

Keywords

  • Algorithms
  • Hip fractures
  • Machine learning
  • Neural network (computer)

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