在部X射线上使用视觉转换器智能识别骨质疏松症
Wei Huang1,2, Pin Pan1,2, Kunpeng Liu3
1Hefei Hospital Affiliated to Anhui Medical University, Hefei, Anhui, 230011, China.
Bone reports
|February 2, 2026
概括
视觉转换器 (ViT) 深度学习模型准确地分类部X射线用于骨质疏松症查. 这种具有成本效益的工具显示了早期检测和辅助诊断的高准确性和通用性.
科学领域:
- 医疗成像中的人工智能
- 深度学习用于骨健康评估
- 放射学和定量成像技术
背景情况:
- 骨质疏松症是一个主要的公共卫生问题,导致骨折风险增加.
- 早期和准确的骨质疏松症诊断对于有效的骨折管理和预防至关重要.
- 当前的诊断方法在可访问性和成本效益方面可能存在局限性,特别是在初级保健机构.
研究的目的:
- 开发和评估使用视觉转换器 (ViT) 架构来分类部X射线图像的深度学习模型.
- 评估模型在区分正常骨质量,骨质疏松症和骨质疏松症的能力.
- 探索ViT在早期骨质疏松症查和辅助诊断方面的潜力.
主要方法:
- 从一家医院和社区诊所回顾3016张部X射线图像的回顾性收集.
- 前期处理和提取近端大腿骨感兴趣的区域 (ROI).
- 对预训练的ViT模型进行微调,用于对ResNet50和InceptionV3进行三类分类和比较,并对内部和外部数据集进行验证.
主要成果:
- 在内部验证组中,ViT模型实现了97.0%的准确性,所有类的AUC都很高 (99.4%-99.9%).
- 外部验证显示准确度为89.4%,AUC从91.6%到98.4%不等.
- 在诊断性能和稳定性方面,ViT模型的表现优于ResNet50和InceptionV3.
结论:
- 基于ViT的深度学习模型表现出卓越的性能和普遍性,用于从部X射线分类骨矿物质密度.
- 这种方法提供了一种具有成本效益和高效的方法,用于使用常规X射线图像进行骨质疏松症查.
- 该模型具有显著的临床实施潜力,特别是在初级保健机构早期检测.
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