(ORDO NEWS) — Researchers from Edith Cowan University ( ECU) have collaborated to create software that can analyze bone density images at unprecedented speed. This software is capable of detecting abdominal aortic calcification (AAC), a condition characterized by the accumulation of calcium deposits in the abdominal aorta, which is a strong indicator of cardiovascular risk.
AAC not only predicts the risk of cardiovascular events such as heart attacks and strokes, but also indicates the likelihood of falls, fractures and dementia later in life. Detection of CRC is usually done through a bone density scan, which is commonly used to diagnose osteoporosis. However, the analysis of these images requires highly qualified specialists and takes a lot of time – from 5 to 15 minutes for each image.
New software developed by ECU researchers can analyze approximately 60,000 images in one day, significantly reducing the time required for analysis. This increase in efficiency is critical for the widespread use of the AAC detection method in research and daily clinical practice.
Associate Professor Joshua Lewis, who participated in the study, believes that the ability to quickly and easily obtain automated CBC estimates from bone density testing could lead to new approaches to the early detection and monitoring of cardiovascular disease.
The study, which involved experts from ECU and other institutions, analyzed more than 5,000 images using both expert analysis and software. The results showed that even in the first version, the program and experts in 80% of cases came to the same conclusion about the severity of AAS.
It is important to note that only 3% of people with high levels of CRC were misdiagnosed as having low levels. These people are at greatest risk of fatal and nonfatal cardiovascular events and all-cause mortality.
Although the accuracy of the measurements compared to human performance has not yet been exhausted, the researchers have already made significant improvements in more recent versions of the software. The ability to screen for cardiovascular disease and other conditions on a large scale before symptoms appear is a promising development.
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