人工智能应用用于骨质疏松症分类使用计算机断层扫描
Wilson Ong1, Ren Wei Liu1, Andrew Makmur1,2
1Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore.
Bioengineering (Basel, Switzerland)
|December 23, 2023
概括
对CT扫描的人工智能 (AI) 分析显示,对诊断骨质疏松症和评估骨矿物质密度 (BMD) 有希望. 这种方法可以为骨质疏松症分类提供一种机会主义的方法,而不需要双能量X射线吸收度 (DXA).
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 骨健康 骨健康 骨健康
背景情况:
- 骨质疏松症是一种严重的健康问题,其特点是骨矿物质密度 (BMD) 低,骨折风险增加.
- 医学成像技术的进步,特别是CT扫描,正在为骨质疏松症的诊断和评估提供新的方法.
研究的目的:
- 审查和评估使用人工智能分析CT扫描用于骨质疏松症分类和BMD分层的有效性,约束和潜在影响.
- 通过CT总结基于AI的骨质疏松症评估相关研究的发现.
主要方法:
- 在主要的电子数据库 (PubMed,MEDLINE,Web of Science,ClinicalTrials.gov) 进行了系统的文献搜索,遵循PRISMA指南.
- 检索和分析了39篇文章,重点是CT成像区域,类型,以及与DXA相比预测BMD的有效性.
主要成果:
- 对CT扫描的AI分析表明,在分类骨质疏松症时,精度 (61.8%-99.4%),灵敏度 (41.0%-100.0%) 和特异性 (31.0%-100.0%) 不同.
- 曲线下的面积 (AUC) 从0.582到0.994不等,这表明AI在这个诊断区域的潜力很大.
结论:
- 用人工智能驱动的CT扫描分析为预测和分类骨质疏松症提供了一种有希望的机会方法.
- 需要进一步的研究来验证临床疗效和可重复性,然后才能在临床实践中进行常规整合.
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