Soon, thanks to artificial intelligence, it will be possible to predict the risk of serious diseases in the fastest time by pressing a button.
Researchers say that soon, thanks to artificial intelligence technology, we will be able to predict the risk of serious health-threatening diseases at the push of a button as soon as possible.
Common bone density scans can quickly detect cardiovascular disease risk. With artificial intelligence, this process will be done much faster and the risks that threaten cardiovascular health will be identified quickly. Also, this technology is effective in the early diagnosis of dementia, and in addition to quickly identifying heart attacks and strokes, it is also used to diagnose brain diseases in old age.
Artificial intelligence makes the process of diagnosing diseases faster
Researchers at Edith Cowan University’s (ECU) School of Medicine and Health Sciences are working on developing software that can analyze bone density scans much faster than experts. This software has the power to scan approximately 60,000 images in one day. “Joshua Lewis”, the director of the project, says that such methods will help prevent future health problems.
She says that these artificial intelligence-based approaches can help in the early diagnosis of cardiovascular diseases faster and in more convenient ways during bone density testing.
This research is one of the largest studies of its kind in this field, and in it, the most common models of bone densification devices are used. The study involves researchers from ECU, the University of WA, the University of Minnesota, Southampton, the University of Manitoba, the Marcus Institute for Aging Research, and Harvard Medical School.
In this study, more than 5000 images have been analyzed by experts and team software. After comparing the results of the experts and the software, it was found that in 80% of the cases, the same result was obtained, but the speed of the software was higher.
Of course, the software model based on artificial intelligence has its own problems that researchers are trying to solve.
