Deep learning for early osteoporosis risk prediction

Tulane UniversityJun 28 2024 Osteoporosis is so difficult to detect in early stage it's called the "silent disease." What if artificial intelligence could help predict a patient's chances of having the bone-loss disease before ever stepping into a doctor's office?

The earlier osteoporosis risk is detected, the more time a patient has for preventative measures. 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.

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