The application progress of artificial intelligence in osteoporosis diagnosis

Abstract

Osteoporosis (OP) is a systemic bone metabolic disorder characterized by a decrease in bone mineral density (BMD) and damage to the trabecular bone microarchitecture. With the increasing global aging population, the incidence of OP has been rising annually, particularly among elderly women, making it a significant public health issue. Traditional diagnostic methods such as dual-energy X-ray absorptiometry (DXA), quantitative computed tomography (QCT), and magnetic resonance imaging (MRI) are effective, but they also have certain limitations. Artificial intelligence (AI) technology is playing an increasingly important role in the management of osteoporosis. Through machine learning (ML), image processing, and data analysis, AI can accurately assess bone density, fracture risk, and other factors, improving the early diagnosis rate of OP and providing strong decision support for clinicians to optimize treatment plans and enhance treatment outcomes. However, it also faces challenges such as AI model interpretability, insufficient diversity in training data, lack of clinical validation, and issues related to privacy protection and ethics. Addressing these problems is crucial for promoting the widespread application of AI technology in this field. As technology continues to advance, AI will become an indispensable part of OP research and clinical applications, driving the development of personalized treatment and precision medicine.

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