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相关概念视频

X-ray Imaging01:24

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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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相关实验视频

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Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts
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从普通X射线预测区域骨矿物质密度的人工智能系统

Huy Gia Nguyen1,2, Dinh-Tan Nguyen1,2, Thach Son Tran1

  • 1School of Biomedical Engineering, University of Technology Sydney (UTS), City Campus (Broadway) Building 11, Level 10, PO BOX 123, Broadway, NSW, 2007, Australia.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
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PubMed
概括

人工智能现在可以通过X射线估计骨密度,为骨质疏松症查提供了DXA扫描的有希望的替代方案. 这种人工智能工具可以准确预测骨折风险,

关键词:
人工智能;骨矿物质密度骨折情况骨质疏松症简单的X光在XBMD

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科学领域:

  • 放射学和医学成像
  • 医疗保健中的人工智能
  • 骨健康和骨质疏松症研究

背景情况:

  • 双能X射线吸收计 (DXA) 是骨矿物质密度评估的黄金标准,但在资源贫困的环境中可用性有限.
  • 骨质疏松症诊断和骨折风险预测对于公共卫生至关重要,但目前的方法面临着可访问性挑战.
  • 标准放射图片已被广泛使用,并有可能用于骨健康评估.

研究的目的:

  • 开发和验证一个人工智能 (AI) 系统,用标准的X射线图像来估计骨矿物质密度 (aBMD).
  • 与DXA相比,评估AI系统在预测骨密度方面的准确性.
  • 评估人工智能系统在识别高风险骨折的个人的有效性.

主要方法:

  • 利用来自越南骨质疏松症研究的数据,包括3783名参与者的7060张数字放射图 (骨盆和脊柱) 和DXA测量.
  • 开发了七种深度学习模型来分析放射图和预测骨矿物质密度,称为"xBMD".
  • 相关AI预测的aBMD (xBMD) 与使用Pearson相关系数测量的aBMD,并使用ROC分析评估断裂风险预测的准确性.

主要成果:

  • 人工智能系统 (xBMD) 与DXA测量的aBMD有很强的相关性:
  • 在识别高风险关骨折的个体时,观察到高精度,AUC值为0. 96 (股骨) 和0. 97 (腰椎).
  • 人工智能的表现在不同年龄组和性别中保持一致.

结论:

  • 人工智能可以从标准放射图中准确预测骨矿物质密度,证明与DXA有很强的相关性.
  • 开发的AI系统有效地识别出高危骨折的个体,显示出高AUC值.
  • 这种人工智能技术为骨质疏松症查提供了一个潜在的,高效的和可访问的替代方案,特别是在资源有限的环境中.