Researchers at Tulane University have developed a new deep learning algorithm to predict osteoporosis risk, outperforming existing methods. The algorithm, tested on over 8,000 participants, showed the best predictive performance compared to other models. The study identified the top 10 factors for predicting osteoporosis risk, including weight, age, gender, and lifestyle factors. The simplified DNN model using these factors performed almost as well as the full model. While more work is needed before the AI platform can be used for public prediction, the study marks a step in that direction. The goal is to provide individuals with accurate osteoporosis risk scores to empower them to seek treatment and prevent further damage. The study was published in Frontiers in Artificial Intelligence, showcasing the potential of AI in early osteoporosis detection and improved patient outcomes.
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#AI model shows promise in detecting osteoporosis #HealthcareTech
