Researchers may have found a more effective way to diagnose osteoporosis.

A recent study by Qiu et al., published in June in Frontiers in Artificial Intelligence, unveiled a deep neural network (DNN) model for early osteoporosis diagnosis. This artificial intelligence (AI) model—developed to address the limitations of existing methods—offers higher accuracy in predicting fracture risk among aging populations. This advancement heralds a new era in osteoporosis management, according to GlobalData.
 
Osteoporosis raises fracture risks in the spine, hip, and forearm, frequently resulting in extended recovery periods and the need for full-time care. As lifespans lengthen, the prevalence of osteoporosis is projected to grow, underscoring the importance of enhanced bone health management and proactive risk reduction strategies.
 
Leveraging diverse patient data, the model significantly enhances early intervention and treatment strategies, promising improved patient care and reduced osteoporosis-related morbidity. Researchers refined the DNN model using machine learning (ML) techniques and compared performance to traditional methods such as support vector machines, finding the new model to be more accurate and reliable for identifying at-risk patients.
 
“This model significantly enhances the ability to identify at-risk patients early, enabling timely intervention and better patient outcomes,” GlobalData Pharma Analyst Sulayman Patel said. “This innovation not only improves diagnosis and treatment strategies but also opens new opportunities for developing targeted therapies and preventive measures, ultimately transforming osteoporosis care for aging populations worldwide.”
 
The DNN model could potentially address the key unmet need of earlier osteoporosis diagnosis and treatment, as identified in GlobalData’s report, “Seven-Market Drug Forecast and Market Analysis.” Osteoporosis is often unnoticed until a fracture occurs, thus being referred to as a “silent disease.” As such, earlier diagnosis is a serious unmet need that needs to be addressed to improve patient outcomes and market growth.
 
“This development comes at a pivotal moment, following the FDA approval of advanced AI models like Avicenna.AI’s CINA-VCF and 16 Bit’s Rho, which have already demonstrated substantial improvements in diagnostic precision and effectiveness. By leveraging similar AI advancements, this DNN model addresses an unfulfilled need in osteoporosis care, ensuring more accurate risk assessments and personalized treatment plans, ultimately leading to better patient outcomes and reduced fracture-related morbidity,” Patel said. “This research underscores how AI is reshaping the osteoporosis market, offering more precise treatment strategies, and accelerating growth. Evidently, there is immense potential in these advancements to drive innovation and improve disease management, making it essential to embrace these technologies for future success in osteoporosis care.”