Revolutionary AI Set To Predict Your Future Health With a Single Click

Artificial Intelligence Health

Researchers from Edith Cowan University developed software that rapidly analyzes bone density scans to detect abdominal aortic calcification (AAC), a predictor of cardiovascular events and other health risks. The software processed images with 80% agreement with experts and could revolutionize early disease detection during routine clinical practice.

Bone density scans can now quickly identify an indicator of cardiovascular health risk.

Thanks to artificial intelligence, we’ll soon have the ability to predict our risk of developing serious health conditions in the future, at the press of a button.

Abdominal aortic calcification (AAC) refers to the buildup of calcium deposits in the walls of the abdominal aorta. It can indicate an increased risk of cardiovascular events, including heart attacks and strokes.

It also predicts your risk of falls, fractures, and late-life dementia. Conveniently, common bone density machine scans used to detect osteoporosis, can also detect AAC. 

However, highly trained expert readers are needed to analyze the images, a process that can take 5-15 minutes per image.

But researchers from Edith Cowan University’s (ECU) School of Science and School of Medical and Health Sciences have collaborated to develop software that can analyze scans much, much faster: roughly 60,000 images in a single day.

Researcher and Heart Foundation Future Leader Fellow Associate Professor Joshua Lewis said this significant boost in efficiency will be crucial for the widespread use of AAC in research and helping people 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,” he said.

Saving a LOT of time

The results were from an international collaboration between 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. Truly a multidisciplinary global effort.

Though it’s not the first algorithm developed to assess AAC from these images, the study is the biggest of its kind, was based on the most commonly used bone density machine models, and is the first to be tested in a real-world setting using images taken as part of routine bone density testing.

It saw more than 5000 images analyzed by experts and the team’s software.

After comparing the results, the expert and software arrived at the same conclusion regarding the extent of AAC (low, moderate, or high) 80 percent of the time – an impressive figure given it was the first version of the software.

Importantly, only 3 percent of people deemed to have high AAC levels were incorrectly diagnosed to have low levels by the software.

“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 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.”

Reference: “Machine learning for abdominal aortic calcification assessment from bone density machine-derived lateral spine images” by Naeha Sharif, Syed Zulqarnain Gilani, David Suter, Siobhan Reid, Pawel Szulc, Douglas Kimelman, Barret A. Monchka, Mohammad Jafari Jozani, Jonathan M. Hodgson, Marc Sim, Kun Zhu, Nicholas C. Harvey, Douglas P. Kiel, Richard L. Prince, John T. Schousboe, William D. Leslie and Joshua R. Lewis, eBioMedicine.
DOI: 10.1016/j.ebiom.2023.104676

The Heart Foundation provided funding for the project, thanks to Professor Lewis’ 2019 Future Leadership Fellowship providing support for research over a three-year period.

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