  [Objective] To develop a Computer-aided diagnosis (CAD) system for plane
frontal hip X-rays with a deep learning model trained on a large dataset
collected at multiple centers. [Materials and Methods]. We included 5295 cases
with neck fracture or trochanteric fracture who were diagnosed and treated by
orthopedic surgeons using plane X-rays or computed tomography (CT) or magnetic
resonance imaging (MRI) who visited each institution between April 2009 and
March 2019 were enrolled. Cases in which both hips were not included in the
photographing range, femoral shaft fractures, and periprosthetic fractures were
excluded, and 5242 plane frontal pelvic X-rays obtained from 4,851 cases were
used for machine learning. These images were divided into 5242 images including
the fracture side and 5242 images without the fracture side, and a total of
10484 images were used for machine learning. A deep convolutional neural
network approach was used for machine learning. Pytorch 1.3 and Fast.ai 1.0
were used as frameworks, and EfficientNet-B4, which is pre-trained ImageNet
model, was used. In the final evaluation, accuracy, sensitivity, specificity,
F-value and area under the curve (AUC) were evaluated. Gradient-weighted class
activation mapping (Grad-CAM) was used to conceptualize the diagnostic basis of
the CAD system. [Results] The diagnostic accuracy of the learning model was
accuracy of 96. 1 %, sensitivity of 95.2 %, specificity of 96.9 %, F-value of
0.961, and AUC of 0.99. The cases who were correct for the diagnosis showed
generally correct diagnostic basis using Grad-CAM. [Conclusions] The CAD system
using deep learning model which we developed was able to diagnose hip fracture
in the plane X-ray with the high accuracy, and it was possible to present the
decision reason.
