# Deep Learning-Based Hip X-ray Image Analysis for Predicting Osteoporosis
## 2 Related Works
### Machine Learning and Deep Learning in Orthopedics  
#### 2.2.1 Osteoporosis Detection  
In recent years, the rise of artificial intelligence has become more common in the medical industry, with machine learning and deep learning as the main applications. In 2016, a study was proposed by [PERSON] et al. that primarily utilized machine learning and deep learning to assist orthopedic surgeons in determining the presence of osteoporosis. The study involved collecting DXA diagnosis results from men aged 50 and above as well as postmenopausal women. The focus of the study was on the femoral neck in hip X-ray images. Unlike our study, which solely utilized hip X-ray images as the dataset, their study incorporated various covariates (e.g., height, age, etc.) as part of the dataset. Under this prerequisite, it is easier to predict better results. They employed an artificial neural network (ANN) to perform binary classification of osteoporosis based on T-scores. The results were promising when evaluating men aged 50 and above and postmenopausal women. In this study, the accuracy for men reached 85.8%, with a sensitivity of 81.6% and a specificity of 90.0%. For women, the accuracy was 86.2%, with a sensitivity of 84.7% and a specificity of 87.7% [12]. In addition, Support Vector Machine (SVM) outperforms Logistic Regression (LR) in predicting osteoporosis risk and surpassed some traditional clinical decision tools, such as the Osteoporosis Self-Assessment Tool (OST), Osteoporosis Risk Assessment Instrument (ORAI), Simple Calculated Osteoporosis Risk Estimation (SCORE), and Osteoporosis Index of Risk (OSIRIS). In 2013, [PERSON] et al. achieved a predictive accuracy of 77%, sensitivity of 78%, and specificity of 76% by collecting medical records of postmenopausal women [13].  
In addition to DXA, in 2021, [PERSON] et al. proposed the application of neural networks for screening application analysis in osteoporosis [14], which utilizes low-frequency radiofrequency data passing through the wrist and uses a multilayer perceptron (MLP) to do the analysis, and in 2020, [PERSON] et al. proposed to train a CNN model based on lumbar spine X-ray images to read osteoporosis and bone loss [15], which are also of great help for orthopedic applications. In 2020, [PERSON] et al. used ResNet, GoogLeNet, and EfficientNet to classify hip X-ray images for osteoporosis [16]. They employed a T-score for binary classification and focused solely on hip X-ray images, achieving the highest accuracy of 84% in the experiments conducted with GoogLeNet and EfficientNet B3. The difference from our study is that in their dataset of 1131 hip X-ray images, 708 were from patients with confirmed hip fractures. Patients with hip fractures typically have lower bone density, which is a key indicator of osteoporosis. Therefore, the ability to distinguish between those with and without osteoporosis was more pronounced in cases of confirmed hip fractures. The difficulty is how to predict the correct bone density in normal hip X-ray images. In the same study of determining whether hip X-ray images are osteoporosis, the experimental model proposed by [PERSON] et al. in 2021 was dichotomized by T-score, and the experimental model mainly uses a deep neural network (DNN) developed based on VGG16 architecture, with an optimal accuracy of 81%, sensitivity of 91%, and specificity of 69% [17]. However, in this past study, only hip X-ray imaging data were included in postmenopausal women clinically at high risk for osteopororic fractures. Furthermore, the data set is limited to women. It does not cover male hip X-rays, where bone density is more difficult to predict.