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相关实验视频

Updated: Jan 18, 2026

Visualizing Motion Patterns in Acupuncture Manipulation
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结构引导的深度学习用于通过骨测量约束来定位背部针点.

Yulong Wang1, Tian Lan2, Wenjian Dou3

  • 1School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, China.

Frontiers in physiology
|September 11, 2025
PubMed
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本研究介绍了传统中医 (TCM) 中精确针点定位的AI框架,将骨测量原理与深度学习相结合,用于准确,实时识别背部针点.

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 传统中国医药 传统中国医药

背景情况:

  • 准确的针点定位对于有效的针和传统中医 (TCM) 疗法至关重要.
  • 目前的方法在不同的身体类型和条件中可能缺乏精度.

研究的目的:

  • 开发和验证一种新的自动化框架,用于使用深度学习识别背部针点.
  • 将传统的TCM骨测量原理与先进的人工智能相结合,以提高精度.

主要方法:

  • 该框架使用HRFormer骨干与结构引导关键点估计模块 (SG-KEM).
  • 一个结构受约束的损失函数确保在标准化的空间坐标系内进行解剖学上一致的预测.
  • 该模型在430张高分辨率的背部图像和19个注释的针点上进行了训练和评估.

主要成果:

  • 该框架实现了0.6%的正常化平均误差 (NME) 和1.2%的失败率 (FR@1厘米).
  • 实时性能在每秒18时被证明具有高精度 (93.8%) 和AUC (0.97).
  • SG-KEM模块和结构受约束损失显著降低了平均误差,在肥胖个体和不同照明条件下显示出强度.

结论:

关键词:
前高级人力资源官员针点本地化定位解剖学地标检测检测 解剖学地标检测人工智能的人工智能是人工智能.骨测量方法 骨测量方法医学成像医学成像

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  • 开发的框架为智能针点定位提供了临床可行性和计算效率高的解决方案.
  • 这种人工智能辅助的方法支持现代TCM的准确诊断和个性化治疗策略.
  • 该研究强调了将传统原则与深度学习相结合的潜力,以推进TCM医疗保健.