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Objectives. The aim of this study was to investigate whether a deepconvolutional neural network (CNN) with an attention module can detectosteoporosis on panoramic radiographs.  Study Design. A dataset of 70 panoramic radiographs (PRs) from 70 differentsubjects of age between 49 to 60 was used, including 49 subjects withosteoporosis and 21 normal subjects. We utilized the leave-one-outcross-validation approach to generate 70 training and test splits.Specifically, for each split, one image was used for testing and the remaining69 images were used for training. A deep convolutional neural network (CNN)using the Siamese architecture was implemented through a fine-tuning process toclassify an PR image using patches extracted from eight representativetrabecula bone areas (Figure 1). In order to automatically learn the importanceof 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 OAscores of 0.667, 0.878 and 0.814, respectively. With the help of the attentionmodule, the OPA, NOPA and OA scores were further improved to 0.714, 0.939 and0.871, respectively.  Conclusions. The proposed method obtained promising results using deep CNNwith an attention module, which might be applied to osteoporosis prescreening.

Osteoporosis Prescreening using Panoramic Radiographs through a Deep Convolutional Neural Network with Attention Mechanism