采用人工智能驱动的机会性骨健康查软件的临床影响,使用常规的X射线检查
Catriona Aileen Syme1, Melanie Lopes1, Alexander Bilbily1,2
116 Bit Inc., 20 Bay Street, 11Th Floor, Toronto, ON, M5J 2N8, Canada.
Skeletal radiology
|February 11, 2026
概括
一个人工智能工具Rho通过在X射线上标记低骨矿物质密度 (BMD) 几乎翻了一番的骨密度测试. 与标准护理相比,Rho生成的测试发现了更多患有低BMD的患者,特别是老年男性.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 骨健康和骨质疏松症查
背景情况:
- 低骨矿物质密度 (BMD) 是骨折的一个重要风险因素.
- 对低BMD的X射线的机会性查可以改善早期检测.
- 人工智能 (AI) 具有提高医学成像诊断能力的潜力.
研究的目的:
- 评估AI设备Rho在X射线中识别低骨矿物质密度 (BMD) 的临床影响.
- 将人工智能促使的骨密度评估与标准护理的诊断产量进行比较.
- 评估人工智能集成对骨密度测试体积的影响.
主要方法:
- 一个人工智能设备Rho分析了50岁及以上患者的X射线,以发现低BMD.
- 放射科医生可以选择在报告中包括Rho标记的发现.
- 在六个月内进行的双能X射线吸收仪 (DXA) 扫描被分类为Rho生成或预先计划的,并比较结果.
主要成果:
- 在34162次X射线中,Rho标记了低BMD的56%;这些发现中有41%被列入报告.
- 在放射科医生报告Rho发现后,初始和监测DXAs分别增加了1.8和1.4倍.
- 罗生成的DXAs发现了较低骨质量 (87%) 和预先计划的DXAs (69%) 的患者比例较高,在老年男性和首次DXA患者中产生了显著的收益率.
结论:
- 将Rho整合到放射学工作流程中显著增加了骨健康评估.
- 通过Rho进行人工智能驱动的机会性查,与标准实践相比,低BMD的诊断产量更高.
- 在特定的人口统计数据中,Rho在识别低BMD方面表现特别有希望,例如老年男性.
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