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

Eur Radiol. 2019 Oct;29(10):5469-5477. doi: 10.1007/s00330-019-06167-y. Epub 2019 Apr 1.


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). 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.

Keywords: Algorithms; Hip fractures; Machine learning; Neural network (computer).

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms*
  • Deep Learning*
  • False Negative Reactions
  • Feasibility Studies
  • Female
  • Hip Fractures / diagnostic imaging*
  • Humans
  • Male
  • Middle Aged
  • Neural Networks, Computer
  • Prognosis
  • ROC Curve
  • Radiographic Image Interpretation, Computer-Assisted / methods
  • Radiography, Abdominal / methods
  • Sensitivity and Specificity
  • Young Adult