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AI algorithm predicts heart disease risk from bone scans
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澳大利亚和加拿大的研究人员开发了一种自动化程序,可以通过常规骨密度扫描识别心血管疾病风险和跌倒风险。该算法通过分析骨密度测试中的椎体骨折评估 (VFA) 图像,评估腹主动脉钙化 (AAC) 的存在和程度,从而快速标记出有心脏病发作、中风和危险跌倒风险的患者。该程序能够在不到一分钟的时间内预测数千张图像的分数,而经验丰富的医生可能需要五到六分钟才能完成。研究发现,58% 接受骨密度扫描的老年人有中度至高度 AAC,其中四分之一的患者未意识到其风险。该算法还可以预测跌倒风险,AAC 评分较高的人跌倒和骨折的风险也更高。

❤️‍🩹 该算法通过分析骨密度测试中的椎体骨折评估 (VFA) 图像来工作,评估腹主动脉钙化 (AAC) 的存在和程度,从而快速标记出有心脏病发作、中风和危险跌倒风险的患者。

⏱️ 该算法速度极快,可以在不到一分钟的时间内预测数千张图像的分数,而经验丰富的医生可能需要五到六分钟才能完成。

🩺 研究发现,58% 接受骨密度扫描的老年人有中度至高度 AAC。甚至更令人担忧的是,其中四分之一的患者完全没有意识到他们升高的风险。

🚶‍♀️ 该算法还可以预测跌倒风险,AAC 评分较高的人跌倒和骨折的风险也更高。研究表明,AAC 与跌倒风险有很强的关联,甚至比其他临床上已知的跌倒风险因素更重要。

Researchers from Edith Cowan University (ECU) and the University of Manitoba have developed an automated program that can identify cardiovascular problems and fall risks from routine bone density scans. 

This could make it considerably easier to detect serious health issues before they become life-threatening.

The algorithm, developed by ECU research fellow Dr. Cassandra Smith and senior research fellow Dr. Marc Sim, works by analyzing vertebral fracture assessment (VFA) images taken during standard bone density tests, which are often part of treatment plans for osteoporosis. 

By assessing the presence and extent of abdominal aortic calcification (AAC) in these scans, the program can quickly flag patients at risk of heart attack, stroke, and dangerous falls.

What’s truly impressive is the speed at which the algorithm works. While an experienced human reader might take five to six minutes to calculate an AAC score from a single scan, the machine learning program can predict scores for thousands of images in less than a minute. 

That level of efficiency could be a significant benefit for healthcare systems looking to screen large populations for hidden health risks.

The need for such screening is evident. In the research, Dr. Smith found that a staggering 58% of older individuals who underwent routine bone density scans had moderate to high levels of AAC.

Even more concerning, one in four of those patients were completely unaware of their elevated risk.

“Women are recognized as being under-screened and under-treated for cardiovascular disease,” Dr. Smith noted. “This study shows that we can use widely available, low-radiation bone density machines to identify women at high risk of cardiovascular disease, which would allow them to seek treatment.”

But the algorithm’s predictive power doesn’t stop at heart health. Using the same program, Dr. Sim discovered that patients with moderate to high AAC scores were also at greater risk of fall-related hospitalizations and fractures compared to those with low scores.

“The higher the calcification in your arteries, the higher the risk of falls and fractures,” Dr. Sim explained. While traditional fall risk factors like previous falls and low bone density are well-known, vascular health is rarely considered. 

“Our analysis uncovered that AAC was a very strong contributor to falls risks and was actually more significant than other factors that are clinically identified as falls risk factors.”

As with any new technology, there are questions to be answered and challenges to overcome before this kind of AI-assisted screening becomes standard practice. 

First and foremost, the algorithm will need to be validated in larger, more diverse patient populations and integrated seamlessly into existing clinical workflows.

However, if those challenges can be met, a simple bone scan – something millions of older adults already undergo regularly – could become an early warning system for some of the most common and devastating health problems we face. 

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AI算法 骨密度扫描 心血管疾病 跌倒风险
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