基于X射线的AI查工具改善了临床环境中早期检测骨密度下降的方法
Andrew N Jayarajah1, Angela Atinga1, Linda Probyn1
1Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, ON, Canada.
概括
一个人工智能 (AI) 工具Rho可以通过X射线识别患有骨质疏松症风险的患者. 这种人工智能被放射科医生很好地采用,并得到了初级保健提供者的积极反,有可能提高查率.
科学领域:
- 肌肉骨健康 肌肉骨健康
- 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:
- 骨质疏松症是一个重要的健康问题,往往被诊断不足.
- 低骨矿物质密度 (BMD) 检测依赖于查,但这种查并不普遍应用.
- 现有的医疗保健基础设施为改善骨质疏松症查提供了机会.
研究的目的:
- 评估人工智能工具 (Rho) 在使用标准X射线识别患有低BMD风险的患者的有效性.
- 评估放射科医生在报告中采用Rho发现的速度.
- 测量初级保健提供者 (PCP) 接受和感知Rho的好处.
主要方法:
- 招募了50岁以上的患者,他们接受了Rho-符合条件的身体部位的X射线.
- 利用人工智能 (Rho) 选X射线以检测潜在的低BMD.
- 收集了患者和PCP的反,并根据DXA扫描计算了正预测值 (PPV).
主要成果:
- 罗检查了987张X射线,标记655张为"罗阳性",用于潜在的低BMD.
- 放射科医生将Rho发现纳入了80%的报告.
- 罗氏在低BMD (T-Score <-1) 中实现了74%的PPV,并导致有关骨健康和新骨质疏松症诊断的讨论增加.
结论:
- 人工智能工具Rho在识别常规X射线中患有低BMD风险的患者方面表现有前途.
- 放射科医生采用和PCP反表明该工具的临床整合潜力.
- 利用现有的X射线数据和AI可以帮助解决低骨质疏松症查率的问题.
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