Researchers have developed a new deep learning algorithm to predict osteoporosis risk.
Osteoporosis is known as the “silent disease” because it is very difficult to detect in the early stages.
What if artificial intelligence could help predict the likelihood of a patient developing osteoporosis before they even walk into a doctor's office?
Researchers have made progress towards that vision with an algorithm that outperforms existing osteoporosis risk prediction methods and could lead to earlier diagnosis and better outcomes for patients at risk of osteoporosis.
Their results are The cutting edge of artificial intelligence.
Deep learning models have garnered attention for their ability to mimic human neural networks and find trends in large datasets without being specially programmed. Researchers tested a deep neural network (DNN) model against four traditional machine learning algorithms and a traditional regression model using data from more than 8,000 participants over the age of 40 from the Louisiana Osteoporosis Study. DNN achieved the best overall predictive performance, measured by scoring each model's ability to identify true positives and avoid mistakes.
“The earlier risk of osteoporosis is identified, the more time patients have to take preventive measures,” said lead author Chuang Qiu, a research assistant professor in the Center for Biomedical Informatics and Genomics at the Tulane University School of Medicine.
“We are pleased to see that our DNN model outperforms other models in accurately predicting osteoporosis risk in an aging population.”
After testing their algorithm on a large sample of real-world health data, the researchers were also able to identify the 10 most important factors in predicting osteoporosis risk: weight, age, sex, grip strength, height, beer intake, diastolic blood pressure, alcohol consumption, years of smoking, and income level.
Notably, the simplified DNN model using these top 10 risk factors performed nearly as well as the full model including all risk factors.
While Chiu acknowledges that there is still a lot of work to be done before AI platforms can be used by the general public to predict an individual's osteoporosis risk, he says identifying the benefits of deep learning models is a step in that direction.
“Our ultimate goal is for people to be able to enter their information, receive a highly accurate osteoporosis risk score, and then receive treatment to strengthen their bones and reduce further damage,” Qiu says.
Source: Tulane University
