Common bone density machine scans, used to detect osteoporosis, can help predict the risk of developing cardiovascular diseases by analysing build-up in the walls of the aorta. They can also predict the risk of falls, fractures, and dementia. But the process takes about five to 15 minutes. Now, artificial intelligence (AI) can do it much faster.
Researchers from Edith Cowan University's (ECU) School of Science and School of Medical and Health Sciences have developed software which can analyse about 60,000 images in a single day. This significant decrease in the required time will be important for widespread use in research and help prevent health issues later in life.
"Since these images and automated scores can be rapidly and easily acquired at the time of bone density testing, this may lead to new approaches in the future for early cardiovascular disease detection and disease monitoring during routine clinical practice," Joshua Lewis, researcher and Heart Foundation Future Leader Fellow said in a press statement by ECU.
Although this is not the first method to assess abdominal aortic calcification (AAC), this research biggest of its kind and is also the first to be tested in a real-life setting using images part of routine bone density testing, according to the statement More than 5000 images were analysed by the software and the experts and the results matched 80% of the time.
Talking about the implications, Lewis said that automated assessment will enable large-scale screening for cardiovascular diseases and other conditions before the symptoms begin to occur.
"This will allow people at risk to make the necessary lifestyle changes far earlier and put them in a better place to be healthier in their later years,” he explained in the statement.
AI is being increasingly used in healthcare to boost research and widen care capabilities. In March, engineers from the University of Waterloo developed an in-home AI tool that monitors elderly people’s activities continuously, without the need for wearable devices, and alert medical experts if they need help. In April, a team of researchers from North Carolina State University developed a tool that improves the ability of electronic devices to detect when a patient is coughing, which can be used in health monitoring, according to Science Daily.
