从检测到分级:一种混合KOA-YOLOv5-RF模型用于膝关节关节炎诊断
Manikandaprabhu Perumalsamy1, Priya Govindarajan1, Rinhas Bran1
1Department of Computer Science, School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, India.
MethodsX
|December 11, 2025
概括
一个新的AI系统使用深度学习和机器学习准确地从X射线中对膝关节关节炎 (KOA) 的严重程度进行评分. 这种工具有助于放射科医生早期检测KOA,特别是在资源有限的地方.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 整形外科 整形外科 整形外科
背景情况:
- 膝关节骨关节炎 (KOA) 的诊断和分级对于有效的患者管理至关重要.
- 准确评估KOA的严重程度,特别是在早期阶段,仍然是一个临床挑战.
- 计算机辅助诊断 (CAD) 系统有望提高诊断效率和准确性.
研究的目的:
- 开发和评估一种新的混合CAD系统,用于从X射线图像中检测和分级KOA的严重程度.
- 将用于本地化/细分的深度学习与用于分类的机器学习相结合.
- 为放射科医生提供临床相关的工具,特别是在资源有限的环境中.
主要方法:
- 开发了一个混合框架,集成YOLOv5用于膝关节细分和Kellgren-Lawrence (KL) 分级的随机森林分类器.
- 该系统在1535张精选和增强的X射线图像的数据集上进行了训练和验证.
- 使用包括整体准确性,ROC-AUC曲线,科恩卡帕和分级敏感性/特异性在内的指标来评估性能.
主要成果:
- 混合CAD系统实现了87%的KL整体分级精度.
- YOLOv5有效地局部化和细分膝关节,减少噪音和聚焦分析.
- 该系统在检测早期KOA (1-2级) 微妙变化的过程中表现出提高的灵敏度.
结论:
- 开发的混合CAD系统是一个可扩展,可解释和临床相关的工具,用于KOA诊断和分级.
- 这种人工智能驱动的方法支持放射科医生在KOA早期检测中,有可能改善患者的治疗结果.
- 该系统在资源有限的环境中的有效性突显了其在更广泛的临床采用方面的潜力.
关键词:
获得医疗保健的便利性计算机辅助诊断 计算机辅助诊断深度学习是一种深度学习.早期疾病检测 早期疾病检测图像细分 图像细分 图像细分关节空间缩小 关节空间缩小机器学习 机器学习医学诊断 医学诊断 医学诊断过程创新是创新的过程.随机的森林 随机的森林这是YOLOv5的.更多相关视频
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