Revolutionizing Cardiovascular Risk Prediction: Bone Density Scans Identify Abdominal Aortic Calcification with AI Software

Revolutionizing Cardiovascular Risk Prediction: Bone Density Scans Identify Abdominal Aortic Calcification with AI Software

Title: Revolutionary Software Analyzes Bone Density Scans to Detect Cardiovascular Health Risks

Subtitle: Researchers from Edith Cowan University develop groundbreaking software that rapidly identifies abdominal aortic calcification, a predictor of heart disease and other health risks.

Date: [Insert Date]

by [Author]

Scientists at Edith Cowan University (ECU) have successfully developed innovative software capable of quickly analyzing bone density scans to detect abdominal aortic calcification (AAC). AAC is a crucial indicator of cardiovascular events and other health risks such as falls, fractures, and late-life dementia. This breakthrough could revolutionize early disease detection during routine clinical practice, potentially saving lives.

AAC refers to the accumulation of calcium deposits in the walls of the abdominal aorta, which can increase the risk of heart attacks and strokes. Normally, bone density scans used to detect osteoporosis can also identify AAC. However, the manual analysis of these images by trained experts can be time-consuming, taking anywhere from 5 to 15 minutes per image.

To combat this, researchers from ECU’s School of Science and School of Medical and Health Sciences collaborated to develop software that can analyze bone density scans at an incredibly rapid rate. The software has the ability to process approximately 60,000 images in a single day – a significant improvement in efficiency that will play a vital role in the widespread use of AAC research and early disease prevention.

Associate Professor Joshua Lewis, a researcher and Heart Foundation Future Leader Fellow, expressed his optimism about the impact of this development. He stated, “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.”

The groundbreaking results were achieved through an international collaboration involving ECU, the University of WA, the University of Minnesota, Southampton, the University of Manitoba, the Marcus Institute for Aging Research, and Hebrew SeniorLife Harvard Medical School. This interdisciplinary effort enabled experts to analyze more than 5000 images alongside the software.

The study concluded that the software achieved an 80% agreement with experts in determining the extent of AAC, categorizing it as low, moderate, or high. Notably, the software only misdiagnosed 3% of individuals with high AAC levels, who were incorrectly deemed to have low levels. This is significant as these individuals face the highest risks of cardiovascular events, all-cause mortality, and further complications.

Professor Lewis emphasized that while there is still room for improvement, subsequent versions of the software have already demonstrated enhanced accuracy compared to human readings. He envisions a future where large-scale screening for cardiovascular disease and related conditions becomes feasible, allowing individuals at risk to make necessary lifestyle adjustments well in advance.

The Heart Foundation provided funding for this project through Professor Lewis’ 2019 Future Leadership Fellowship, supporting his research endeavors over a three-year period.

The study, titled “Machine learning for abdominal aortic calcification assessment from bone density machine-derived lateral spine images,” was published in the journal eBioMedicine. The research team hopes that their findings will pave the way for earlier disease detection and better health outcomes for individuals worldwide.

As artificial intelligence continues to advance, the integration of such technologies could bring about a new era of preventive medicine, allowing individuals to proactively manage their health by predicting potential health conditions at the press of a button.


Leave a Comment

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Recent News

Editor's Pick