Health, a fundamental aspect of human life, is considered one of the most crucial assets we possess. As stated by Hippocrates, "Health is the greatest wealth." In the face of advancing technologies, scientists are striving to develop tools that predict an individual's future health, providing means for early intervention before serious conditions arise. In this context, a scientific team from Edith Cowan University (ECU) has created unique software utilizing artificial intelligence (AI) to analyze bone density images, paving the way for rapid diagnosis of potential cardiovascular threats.

Aortic Abdominal Calcification (AAC) is a condition characterized by the accumulation of calcium deposits in the abdominal aorta, serving as a strong indicator of cardiovascular diseases. The analysis of bone density images, typically the basis for AAC diagnosis, has been a labor-intensive process, requiring highly qualified specialists to spend 5 to 15 minutes analyzing each image. The new software resulting from ECU's research can analyze around 60,000 images in a single day, significantly reducing the time needed for analysis. This technological breakthrough holds key implications for the widespread application of AAC detection methods, both in scientific research and daily clinical practice.

Associate Professor Joshua Lewis, a participant in the study, notes that the ability to quickly and easily obtain automated AAC estimates based on bone density scans could revolutionize the approach to early detection and monitoring of cardiovascular diseases. In an experiment conducted by the ECU research team and collaborating institutions, over 5000 images were analyzed using both expert analysis and the developed software. The study results showed that in 80% of cases, both the program and experts reached the same conclusion regarding the severity of AAC. It was observed that only 3% of individuals with a high level of AAC were incorrectly diagnosed as having a low level of this condition, and these individuals are most vulnerable to fatal cardiovascular events and all-cause mortality.

Although the comparison of the accuracy of the new software with human performance has not been fully verified, researchers have made significant improvements in newer versions of the software. The prospect of large-scale screening for cardiovascular diseases and other conditions before symptoms appear seems to be a promising achievement. Professor Lewis emphasizes that the new technology will enable individuals at risk to make necessary lifestyle changes earlier, significantly impacting their long-term health.

These achievements demonstrate the undeniable potential that the development of artificial intelligence technology holds in the field of medicine. By creating effective tools for early diagnosis of cardiovascular diseases, scientists contribute to better understanding and managing the risks associated with these conditions, ultimately leading to an improvement in the quality of life for many individuals.