Bone density machine scans used to detect osteoporosis can also be used to detect AAC, however, this requires highly trained experts to read and analyze the images which can take 5 to 15 minutes per image. Researchers from the Edith Cowan University (ECU) School of Science and the School of Medical and Health Sciences have collaborated to develop software that can analyze these types of scans at a much faster rate to evaluate roughly 60,000 images a day. This significant boost in efficiency could be crucial to the widespread use of AAC in research to help people possibly avoid developing health problems 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,” said researcher and Heart Foundation Future Leader Fellow Associate Professor Joshua Lewis.
This study was an international multidisciplinary collaboration between researchers from ECU, the University of Manitoba, the Marcus Institute for Aging Research, the Hebrew Senior Life Harvard School, the University of Minnesota, Southampton, and the University of WA.
While this may not be the first algorithm designed to assess ACC from these types of images, this study is the biggest of its kind, based on the most commonly used bond dentist machine models, and it is also the first to be tested in real-world settings using images taken as part of a regular routine bone density testing.
This algorithm saw over 5000 images which were also analyzed by experts, and after comparing results all arrived at the same conclusions for the extent of ACC (low, moderate, or high) 80% of the time. 3% of the people were incorrectly diagnosed as having low levels of ACC by the software which was deemed as being high levels of AAC by human experts. In all the researchers believe that this was impressive being that it was the first version of the software, and the findings have been published in eBioMedicine.
“This is notable as these are the individuals with the greatest extent of disease and highest risk of fatal and nonfatal cardiovascular events and all-cause mortality,” Professor Lewis said.
“Whilst there is still to work to do to improve the software’s accuracy compared to human readings, these results are from our version 1.0 algorithm, and we already have improved the results substantially with our more recent versions.
“Automated assessment of the presence and extent of AAC with similar accuracies to imaging specialists provides the possibility of large-scale screening for cardiovascular disease and other conditions — even before someone has any symptoms.”
“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.”
