# Deep Learning-Based Hip X-ray Image Analysis for Predicting Osteoporosis
## 5 Conclusions  
Most people are often troubled by osteoporosis as they get older. Since the condition is not obvious in the early stages, people often ignore that their BMD is decreasing with age, and it is important to promote the concept of bone protection and early prevention and treatment. In recent years, convolutional neural networks have been widely used in different fields of research, and the number of medical image analyses is increasing year by year, such as in thoracic medicine, dermatology, ophthalmology, orthopedics, dentistry, etc. Artificial intelligence is expected to be able to promote healthcare in a variety of ways, not only in the diagnosis of the patient and the development of medicines but also to become a good assistant to the doctor, providing better and more personalized medical services so that people can get better healthcare. Through the analysis of hip X-ray images, this study constructed two sets of deep learning models for automatic segmentation and classification of X-ray images, which can be used as a reference for osteoporosis assessment and diagnosis. In this study's image segmentation results, using the U-Net and U-Net++ construction models, the IoU results of the femoral neck, wards triangle, and total hip showed similar results, and the 0.85 of the U-Net++ in the greater trochanter was better than the 0.78 of the U-Net, and all the segmented IoU were greater than equal to 0.5, which can be regarded as a valid segmentation result. In the experiments on image classification, due to the insufficient amount of raw data, the model complexity is too low and the number of image features is too small, which indirectly leads to model underfitting. The accuracy of the experiments can be improved by data augmentation, and the data augmentation and the addition of the dropout layer in the model are also helpful for the subsequent experiments to prevent overfitting. Using the T-score as a basis for classification, the model with DenseNet121 and without U-Net++ image segmentation has the highest accuracy of 74% on the total hip, and the F1-score is 71%. In the deep learning model comparison, most of the VGG16 experimental accuracies are a bit higher than both DenseNet121 and ResNet50, indicating that instead of using a deeper neural network, the simpler VGG16 model can perform well in the problem of hip X-ray image classification. In the best experimental results, the accuracy of total hip classification was 74% for both T-score and BMD. Using the overall hip image as the basis for osteoporosis was more consistent with the orthopedic surgeon's diagnosis of the hip. The contribution of this study lies in the establishment of an automated X-ray image segmentation model and an automated model for reading X-ray images of osteoporosis, hoping to provide some assistance to orthopedic surgeons in the diagnosis of osteoporosis. Moreover, the cost of DXA is relatively high. As the middle-aged and old-aged population is increasing year by year, the number of people with osteoporosis is surely increasing year by year, but DXA is only available in higher-grade hospitals or nursing homes. Patientswith osteoporosis in remote areas can only rely on ultrasound or other simpler instruments, which are less accurate than DXA. It is hoped that the automated image segmentation and classification model developed in this study can be provided to remote hospitals or nursing homes in the future so that deep learning can be utilized in osteoporosis diagnosis and treatment. There are some research limitations in this study. The dataset used and established by the method proposed in this study is limited. The source of X-ray images for this study was patients in a regional hospital in Taiwan from September 2020 to September 2021; a total of 139 left and right hip radiographs were collected. The limited quantity of data may be attributed to ethical considerations surrounding the collection of medical imaging data.