![]() ![]() The external validation set confirmed a model performance with an overall accuracy of 71.8% and an AUC value of 0.700. The area under the ROC curve value was 0.867, indicating a reasonable performance for screening osteoporosis by simple hip radiography. The PPV was 78.5%, and the NPV was 86.1%. Our final DNN model showed an overall accuracy of 81.2%, sensitivity of 91.1%, and specificity of 68.9%. Additionally, we performed external validation using 117 datasets. A gradient-based class activation map (Grad-CAM) overlapping the original image was also used to visualize the model performance. We drew the receiver operating characteristic (ROC) curve. ![]() We calculated the confusion matrix and evaluated the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). ![]() Based on VGG16 equipped with nonlocal neural network, we developed a deep neural network (DNN) model. The 1001 images were randomly divided into three sets: 800 images for the training, 100 images for the validation, and 101 images for the test. Of these, 504 patients had osteoporosis (T-score ≤ − 2.5), and 497 patients did not have osteoporosis. A total of 1001 datasets of proximal femur DXA with matched same-side cropped simple hip bone radiographic images of female patients aged ≥ 55 years were collected. This study aimed to predict osteoporosis via simple hip radiography using deep learning algorithm. Despite being the gold standard for diagnosis of osteoporosis, dual-energy X-ray absorptiometry (DXA) could not be widely used as a screening tool for osteoporosis. ![]()
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