骨折风险得分使用输出从一个机会性屏幕的低骨密度从传统的X射线的X射线的得分
Catriona A Syme1, Mark D Cicero1,2, Jonathan D Adachi3
116 Bit Inc, ON, Canada.
概括
由人工智能驱动的软件通过X射线估计骨密度,改善骨折风险评估. 这种新的方法提高了FRAX得分,在DXA扫描无法使用时提供了有价值的工具.
科学领域:
- 骨质疏松症的研究研究.
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
背景情况:
- 骨折风险评估对于预防骨质疏松性骨折至关重要.
- FRAX是一种广泛使用的工具,用于估计10年骨折风险.
- 用DXA测量的部 (FN) 骨矿物质密度 (BMD) 细化了FRAX得分.
研究的目的:
- 评估人工智能估计的FN BMDT分数 (RhoTM) 在改善FRAX分数方面的实用性.
- 将FRAX分数结合AI估计 (FRAX-Rho) 与使用DXA衍生T分数 (FRAX-DXA) 和没有T分数 (FRAX-NoT) 的FRAX分数进行比较.
主要方法:
- 加拿大多中心骨质疏松研究队列的回顾性分析.
- 参与者使用腰部/胸部脊柱X射线,FRAX风险因子和DXAT分数.
- 在9年内,FRAX-Rho,FRAX-DXA和FRAX-NoT对严重骨质疏松性骨折 (MOF) 和骨骨折进行了分类和预测准确性的比较.
主要成果:
- 在MOF风险分类中,FRAX-Rho与FRAX-DXA非常一致 (kappa=0.80).
- 对于关节骨折风险,FRAX-Rho和FRAX-DXA之间的协议明显优于FRAX-NoT和FRAX-DXA.
- 与FRAX-NoT相比,FRAX-Rho和FRAX-DXA在事件MOF方面表现出更高的区分能力.
结论:
- 来自传统X射线的AI估计的FNT分数为FRAX评估增加了价值.
- 当DXA无法访问时,FRAX-Rho为骨折风险评估提供了一个可行的替代方案.
- 这种由人工智能驱动的方法显示出在骨质疏松症管理中更广泛应用的希望.
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