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? Tulane University researchers made progress toward that vision by developing a new deep learning algorithm that outperformed existing computer-based osteoporosis risk prediction methods, potentially leading to earlier diagnoses and better outcomes for patients with osteoporosis risk. Their results were recently published in Frontiers in Artificial Intelligence .
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. We were pleased to see our DNN model outperform other models in accurately predicting the risk of osteoporosis in an aging population." Chuan Qiu, lead author, research assistant professor at the Tulane School of Medicine Center for Biomedi.