Edith Cowan University (ECU) researchers have developed an innovative automated machine-learning algorithm to accurately assess abdominal aortic calcification (AAC) during routine bone density testing. AAC is a critical indicator of advanced vascular disease.
Vascular diseases, including those affecting the abdominal aorta, are a significant health concern. Detecting and assessing abdominal aortic calcification is crucial for understanding the risk of cardiovascular events, falls, fractures, dementia, and mortality.
A study led by Dr. Jack Dalla Via, a Postdoctoral Research Fellow at ECU, aimed to create an automated algorithm capable of efficiently assessing AAC during routine bone density testing.
The researchers wanted to determine if the algorithm’s results would align with those obtained through manual assessment by experts.
The automated algorithm demonstrated impressive accuracy and efficiency in assessing AAC. Key findings of the study include:
The algorithm significantly reduced the time needed to screen for AAC. It takes less than a minute to predict AAC scores for hundreds of images, compared to the five to six minutes required for manual assessment by an experienced reader.
Women with moderate to extensive AAC, as determined by the algorithm, had a higher risk of fall-related hospitalization and clinical fractures compared to those with low AAC.
This study is groundbreaking because it reveals that AAC, when assessed automatically, can identify older women at an increased risk of falls and fractures.
The automated algorithm has the potential to revolutionize clinical practice in several ways:
AAC assessment can seamlessly integrate bone density testing, providing valuable information about cardiovascular risk, dementia, and mortality.
The algorithm’s accuracy levels are comparable to manual assessments by experts.
Routine bone density testing involving minimal radiation exposure can become an opportune screening tool for various health outcomes beyond bone health.
The next phase involves verifying the algorithm’s performance in independent cohorts, particularly within routine clinical bone densitometry services. This step is essential to ensure the algorithm’s reliability and applicability in real-world healthcare settings.
The breakthrough research has garnered interest from national and international companies keen on commercializing this innovative product.
This suggests that the algorithm’s benefits may soon be accessible to a wider audience, potentially enhancing healthcare practices and patient outcomes.
In conclusion, the automated algorithm developed by ECU researchers represents a significant advancement in healthcare technology.
It offers a swift and accurate way of assessing AAC, providing crucial insights into vascular disease risk and associated health outcomes.
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The research findings can be found in Journal of Bone and Mineral Research.
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