A groundbreaking study from Tulane University has developed an AI-based model to predict an individual’s likelihood of developing osteoporosis. The model analyzed real-world health data from over 8,000 participants aged 40 and older, identifying ten critical factors for assessing osteoporosis risk. These factors include weight, age, grip strength, blood pressure, and habits like smoking and alcohol consumption. The model, powered by deep learning, mimics the human brain to detect patterns in large datasets, potentially leading to earlier diagnoses and better outcomes for those at risk of osteoporosis.
Lead author Chuan Qiu emphasized the importance of early detection, stating that the earlier osteoporosis risk is detected, the more time a patient has for preventative measures. However, the model is still in development and requires further refinement before it can be publicly available. The ultimate goal is to provide individuals with highly accurate osteoporosis risk scores to empower them to seek treatment and strengthen their bones.
This advancement in predicting osteoporosis risk holds promise for improving early detection and management of the condition, potentially benefiting many individuals in maintaining better bone health. The study was published in the journal Frontiers in Artificial Intelligence.
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