Automated osteoporosis prediction system using artificial intelligence to calculate cortical thickness index from hip X-rays

Abstract

Early diagnosis and regular monitoring of osteoporosis is key to prevent further deterioration and fractures in osteoporosis patients. Dual-energy X-ray Absorptiometry (DXA), despite being a gold standard for diagnosing osteoporosis, is not routinely ordered due to limited availability of DXA machine, especially in developing countries. As a result, orthopedists often lack DXA results at the time of examination. This study aims to develop an automated AI system to predict osteoporosis based on a plain x-ray scan of patient’s femur and demographic data, such as age, height and weight. The system first performs instance segmentation on the X-ray scan to locate femur, followed by image processing techniques to measure the inner and outer diameter of the femur, and then compute cortical thickness index (CTI). The CTI value, together with patient’s demographic data, is incorporated into a classification model to predict if the patient is suffering from osteoporosis. We found that the CTI calculated by the AI system is comparable to the manually calculated CTI. The AI system can predict at an accuracy of 85.3% using CTI and patient data.