# Deep Learning-Based Hip X-ray Image Analysis for Predicting Osteoporosis
## 1 Introduction  
Osteoporosis is one of the most common issues in orthopedic medicine today. People have been facing the challenges of osteoporosis due to the natural process of aging. According to the International Osteoporosis Foundation (IOF), both men and women over the age of 50 have a significant risk of developing osteoporosis, with approximately one-fifth of men and one-third of women falling into this category [1]. The risk of osteoporosis dramatically increases in women after menopause. It is estimated that there are approximately 200 million women worldwide who are affected by osteoporosis. As our country is gradually transitioning into an aging society, the percentage of people afflicted by osteoporosis is steadily on the rise. Consequently, effective prevention and treatment of osteoporosis have become vital concerns in the field of orthopedic medicine [2]. In the early stages of osteoporosis, there are no obvious symptoms, but fractures may occur because of minor injuries [3]. In severe cases, fractures may occur not only in the hip but also in the spine, wrist, arm, and knee. Ultrasound [4], peripheral bone densitometry, etc., are themain instruments used by orthopedic surgeons to diagnose osteoporosis. However, the image of the hip formed by these methods is more difficult for the orthopedic surgeon to read with his/her own eyes and requires the use of more sophisticated instruments, such as dual-energy X-ray absorptiometry (DXA) [5]. The bone mineral density (BMD) of the hip is calculated and compared to the T-score of a younger, healthier person to diagnose osteoporosis [6]. Currently, in the clinical diagnosis of the orthopedic hip, physicians base their diagnosis of osteoporosis on a DXA report of the femoral neck in the hip and the total hip. BMD from DXA is the main criterion for diagnosing osteoporosis. In Taiwan, because DXA is expensive and limited in number, the popularity of the equipment is far less than that of X-ray imaging, and only high-grade hospitals or nursing homes are equipped with this equipment, while in some remote areas or villages, it is not convenient to use this resource. In some remote areas or rural areas, it is not convenient to use this resource, and cheaper equipment such as ultrasound and peripheral densitometry is mostly used for diagnosis, resulting in a less accurate and less efficient diagnosis of symptoms. Currently, physicians are unable to manually read X-ray images for osteoporosis in clinical diagnosis. Early diagnosis of osteoporosis is important for the prevention of osteopororotic fractures, and in recent years, artificial intelligence has been gradually introduced into medical diagnosis [7]. As machine learning and deep learning methods have made significant advances in image recognition, it is worthwhile to revisit the question of whether or not it is possible to use convolutional neural networks (CNNs) to read a hip X-ray image and further predict a patient's BMD status, and the answer may be affirmative. While traditional machine learning methods can be effective, they rely on manual feature extraction, sequential training of the model, and output of the results. With the great leap in hardware computing power of graphics processing units (GPUs), deep learning can directly input data into the model, and the neural networks will extract the features by themselves when they train the model and then output the results. Compared with machine learning, deep learning omits the part about manual feature extraction. When the amount of data is relatively large, deep learning can extract more features, and the output results will be better than traditional machine learning. In this study, we collected data from 134 orthopedic patients, most of whom were menopausal women and elderly men. The data collected were patients' left or right hip X-ray images and DXA diagnostic data, of which the DXA diagnostic reports were femoral neck, greater trochanter, Ward's triangle, and total hip BMD and T-score.