使用胸部放射和人工神经网络进行骨质疏松精确查:OPSCAN随机对照试验
Chin Lin1, Dung-Jang Tsai1, Chih-Chia Wang1
1From the Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, ROC (C.L.); Department of Artificial Intelligence (C.L., D.J.T., W.H.F.), Department of Family and Community Medicine (C.C.W., Y.P.C., J.W.H., W.H.F.), and Division of Cardiology, Department of Internal Medicine (C.S.L.), Tri-Service General Hospital, National Defense Medical Center, No. 325, Sec. 2, Chenggong Rd, Neihu District, Taipei TW 114, ROC; School of Public Health, National Defense Medical Center, Taipei, Taiwan, ROC (C.L., D.J.T.); and Department of Statistics and Information Science, Fu Jen Catholic University, Taipei, Taiwan, ROC (D.J.T.).
人工智能 (AI) 识别了骨质疏松症查的高风险个人. 与常规护理相比,人工智能支持的胸部X射线查显著提高了这一群体的骨质疏松症检测率.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 骨质疏松症查 骨质疏松症查
背景情况:
- 骨质疏松症的诊断是具有挑战性的,因为无症状的呈现.
- 查高风险人群对于早期检测至关重要.
研究的目的:
- 评估双能X射线吸收计 (DXA) 查骨质疏松症的有效性.
- 评估一种人工智能 (AI) 模型,用于识别使用胸部X射线图的高风险个体.
主要方法:
- 一项随机对照试验包括40岁以上的参与者,他们接受了胸部X射线扫描.
- 由AI识别的高风险参与者被随机分配到查组 (提供DXA) 或对照组 (常规护理).
- 后勤回归分析了两组之间新发性骨质疏松症的差异.
主要成果:
- 人工智能在40,658名参与者中确定了12.1%的高风险.
- 查组显示骨质疏松症检测率显著更高 (11.1%与1.1%相比; OR,11.2; P < .001).
- 人工智能识别的高风险参与者不符合正式的DXA标准,其骨质疏松症诊断的几率大幅增加 (OR, 23.2).
结论:
- 启用AI的胸部X射线扫描有效地识别了患骨质疏松症高风险的个体.
- 这种方法在目标人群中显著提高了骨质疏松症诊断率.
- 人工智能有助于更早,更有效地检测骨质疏松症,特别是在那些不符合传统查标准的人群中.
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