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使用CT成像评估骨质疏松症的人工智能:一个范围审查

Hanwen Cheng¹, Yajun Zhang², Meng Meng¹

¹Department of Orthopaedic Trauma, Peking University People's Hospital, Peking University, Beijing, China.

Frontiers in medicine

|March 12, 2026

概括

应用于CT扫描的人工智能 (AI) 对骨质疏松症诊断和查有希望,与DXA和QCT相比. 需要进一步的研究来改善骨折风险预测,并为临床使用标准化AI工作流程.

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
骨质疏松症研究骨质疏松症研究

背景情况:

骨质疏松症的评估传统上依赖于双能X射线吸收计 (DXA) 和定量CT (QCT).
计算机断层扫描 (CT) 成像为机会性骨质疏松症评估提供了一个潜在的替代方案.
人工智能 (AI) 的整合为分析骨健康的CT数据提供了新的途径.

研究的目的:

系统地审查和绘制基于CT的骨质疏松症评估中人工智能应用的当前研究.
专注于人工智能在这个领域的方法方法,解剖区域和算法性能.
识别现有文献中的趋势和差距.

主要方法:

在PubMed,EMBASE和Web of Science数据库 (1995-2025) 中进行了全面的文献搜索.
包括使用人工智能,机器学习或深度学习对CT图像进行骨质疏松症分类,骨矿物质密度 (BMD) 估计或骨折风险预测的研究.
数据提取的重点是研究特征,成像来源,分析工作流程和验证方法.

主要成果:

包括51项研究,主要是回顾性和单一中心研究,其中许多来自中国.
人工智能模型在骨质疏松症诊断 (AUC 0.80-0.997) 和机会性查 (AUC 0.781-0.99) 中表现出高性能.
骨折风险预测模型显示了更高的可变精度 (AUC 0.702-0.92),并且在研究中观察到显著的方法异质性.

结论:

人工智能增强的CT显示了与DXA和QCT等既定方法相比的诊断和查性能.
方法的多样性和不一致的验证策略目前限制了人工智能工具的通用性和临床翻译.
标准化人工智能工作流程和整合多模式数据可以提高骨折风险预测,并促进骨质疏松症管理的广泛临床采用.

关键词:

人工智能的人工智能是人工智能.计算机断层扫描 (CT) 是一种计算机断层扫描.诊断诊断诊断诊断诊断诊断骨质疏松症是一种骨质疏松症.审查审查审查审查审查审查

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