Using artificial intelligence to diagnose osteoporosis in hospital patients
Flinders researchers will develop an artificial intelligence system to identify hospital patients at high risk of osteoporosis.
Osteoporosis affects one million Australians and is associated with an increased risk of fractures following little or no trauma. Post-menopausal women and the elderly are at particularly risk of developing osteoporosis.
There are effective medications for treating osteoporosis, and timely intervention can reduce the risk of future fractures by up to 70 per cent and death by 11 per cent.
However, a significant gap exists in the treatment of patients who present to hospital with minimal trauma fractures and haven’t previously been diagnosed with osteoporosis. The majority of these patients are not assessed, nor appropriately managed for the condition.
Dr Minh-Son To has received a Flinders Foundation Health Seed Grant to improve the diagnosis of osteoporosis in patients who present to hospital, using routinely obtained clinical data.
Dr To’s team will develop a new artificial intelligence tool to opportunistically screen for osteoporotic risk in hospital patients.
“We will develop and prospectively evaluate an artificial intelligence system for identifying patients at high risk of osteoporosis. The artificial intelligence utilises neural networks to extract complex features from data, make predictions about the patients, and estimate osteoporosis risk,” Dr To said.
“A key advantage of our approach is that this additional information about osteoporosis can be extracted without any extra cost or inconvenience to the patient.
“Identifying patients at risk of osteoporotic fractures will enable preventative measures to be implemented, such as vitamin D and calcium therapy or medications to improve bone health, which have the potential to significantly reduce the morbidity and mortality burden of these fractures.”
Research category: Public Health
Project title: Improving identification of hospital patients with poor bone health using artificial intelligence
Lead researcher: Dr Minh-Son To
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