New Delhi, June 29 (IANS) Researchers have developed a new deep learning algorithm that outperforms existing computer-based methods for predicting 'osteoporosis' risk, which could lead to earlier diagnosis and better outcomes for patients at risk of osteoporosis, a new study has found.
Osteoporosis is a bone disease caused by a decrease in bone density and mass and changes in bone structure and strength, which weakens bones and increases the risk of fractures.
In the study, published in the journal Frontiers in Artificial Intelligence, the researchers used data from more than 8,000 participants aged 40 and older from the Louisiana Osteoporosis Study to test their deep neural network (DNN) model against four traditional machine learning algorithms and a traditional regression model.
DNNs achieved the best predictive performance overall, 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 Chuang Chiu, lead author of the study and a research assistant professor at the Tulane University School of Medicine Center.
“We are pleased to see that our DNN model outperforms other models in accurately predicting the risk of osteoporosis in an aging society,” he added.
By testing their algorithm on a large sample of real-life 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.
“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 said.
