  Objectives. The aim of this study was to investigate whether a deep
convolutional neural network (CNN) with an attention module can detect
osteoporosis on panoramic radiographs.
  Study Design. A dataset of 70 panoramic radiographs (PRs) from 70 different
subjects of age between 49 to 60 was used, including 49 subjects with
osteoporosis and 21 normal subjects. We utilized the leave-one-out
cross-validation approach to generate 70 training and test splits.
Specifically, for each split, one image was used for testing and the remaining
69 images were used for training. A deep convolutional neural network (CNN)
using the Siamese architecture was implemented through a fine-tuning process to
classify an PR image using patches extracted from eight representative
trabecula bone areas (Figure 1). In order to automatically learn the importance
of different PR patches, an attention module was integrated into the deep CNN.
Three metrics, including osteoporosis accuracy (OPA), non-osteoporosis accuracy
(NOPA) and overall accuracy (OA), were utilized for performance evaluation.
  Results. The proposed baseline CNN approach achieved the OPA, NOPA and OA
scores of 0.667, 0.878 and 0.814, respectively. With the help of the attention
module, the OPA, NOPA and OA scores were further improved to 0.714, 0.939 and
0.871, respectively.
  Conclusions. The proposed method obtained promising results using deep CNN
with an attention module, which might be applied to osteoporosis prescreening.
