Osteoporosis, often dubbed the "silent disease" due to its challenging early detection, may soon benefit from predictive artificial intelligence. Researchers have advanced this possibility with a new deep learning algorithm that surpasses current computer-based methods for predicting osteoporosis risk. This innovation could enable earlier diagnoses and improved outcomes for at-risk patients. () Their results were recently published in Frontiers in Artificial Intelligence.
‘Approximately 80% of #osteoporosis patients are women. Now, the use of #AI to predict osteoporosis risk represents an intriguing advancement. #bonehealth’ Deep Learning Models in Osteoporosis Research
Deep learning models have gained notice for their ability to mimic human neural networks and find trends within large datasets without being specifically programmed to do so. Researchers tested the deep neural network (DNN) model against four conventional machine learning algorithms and a traditional regression model, using data from over 8,000 participants aged 40 and older in the Louisiana Osteoporosis Study. The DNN achieved the best overall predictive performance, measured by scoring each model’s ability to identify true positives and avoid mistakes.
“The earlier osteoporosis risk is detected, the more time a patient has for preventative measures,” said lead author Chuan Qiu, a research assistant professor at the Tulane School of Medicine Center for Biomedical Informatics and Genomics. “We were pleased to see our DNN model outperform other models in accurately predicting the risk of osteoporosis in an aging population.”
In testing the algorithms using a large sample size of real-world health data, the researchers were also able to identify the 10 most important factors for predicting osteoporosis risk: weight, age, gender, grip strength, height, beer drinking, diastolic pressure, alcohol drinking, years of smoking, and income level.
Diet for Osteoporosis
The diet for osteoporosis includes bone-strengthening foods like dairy products, fish, fruits, and vegetables, along with adhering to a healthy lifestyle.Notably, the simplified DNN model using these top 10 risk factors performed nearly as well as the full model which included all risk factors.
While Qiu admitted that there is much more work to be done before an AI platform can be used by the public to predict an individual’s risk of […]
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