上肢整形医学的计算机视觉:对基于成像的算法进行骨折检测和放射测量的广泛审查
Nimra Akram1, Bilal Qaddoura2, Donia Karimaghaei3
1Trauma and Orthopaedics, Royal Berkshire NHS Foundation Trust, Reading, GBR.
Cureus
|January 8, 2026
概括
计算机视觉,特别是深度学习,在分析上肢医学图像以检测骨折和测量方面表现有前途. 然而,广泛的临床采用需要更大的数据集和这些自动化骨科工具的潜在验证.
科学领域:
- 整形外科手术 整形外科手术
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 计算机视觉 (CV) 在骨科医学成像中越来越重要.
- 深度学习 (DL) 增强了对骨科成像的自动解释.
- 上肢骨科严重依赖于成像用于诊断和规划.
研究的目的:
- 绘制和描述CV在上肢骨科的当前应用.
- 在文献中描述临床问题,成像方式和方法.
- 评估骨科成像中自动化解释的可行性和准确性.
主要方法:
- 系统的文献搜索 欧维德·梅德林和Embase (1995-2025年).
- 包括使用自动/半自动CV技术对上肢图像进行诊断/规划的研究.
- 通过两个独立审查员使用PRISMA-ScR框架提取数据.
主要成果:
- 16项研究符合纳入标准,主要关注手腕和肩膀成像.
- 常见的应用包括基于放射的骨折检测和形态测量评估.
- 像CNN,YOLO和U-Net这样的深度学习模型在分类和测量任务中显示出高性能.
结论:
- 上肢骨科的CV处于早期,有前途的阶段.
- 自动骨折检测和测量工具显示出令人鼓舞的准确性.
- 临床实施受到小型数据集,缺乏验证和不一致的报告的阻碍;未来的研究应该集中在多中心研究和软组织成像上.
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