AI-Powered Model Predicts Osteoporosis Risk with High Accuracy

Researchers at Tulane University have developed an AI-based model to predict the risk of developing osteoporosis. Tested on real-world health data, the model identifies key risk factors including weight, age, and habits. The deep learning algorithm aims for early detection and better patient outcomes.

In a groundbreaking development, researchers at Tulane University have unveiled an AI-based model designed to predict an individual's risk of developing osteoporosis. This condition leads to weakening and brittle bones.

The model, tested on real-world health data, identifies ten crucial risk factors—including weight, age, and grip strength. Additional factors considered are blood pressure, smoking, and alcohol habits.

The deep learning algorithm, derived from data of over 8,000 participants aged 40 and older in the Louisiana Osteoporosis Study, mimics human brain functions to identify trends in large datasets. According to the study published in Frontiers in Artificial Intelligence, early detection of osteoporosis risk could enable timely preventive measures and better patient outcomes.

Lead researcher Chuan Qiu noted that while the model requires further refinement, the ultimate goal is to create a public platform where people can input their information and receive accurate osteoporosis risk scores. This would empower individuals to seek treatment proactively, strengthening their bones and mitigating damage.

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