Wang Yulong, Lan Tian, Dou Wenjian, Chen Zhi, Zhang Song, Chen Gong
School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, China.
School of Computer Science, Inner Mongolia University, Hohhot, China.
Front Physiol. 2025 Aug 26;16:1662104. doi: 10.3389/fphys.2025.1662104. eCollection 2025.
Accurate acupoint localization is crucial for the effectiveness of acupuncture and related Traditional Chinese Medicine (TCM) therapies. This study introduces a novel automated framework for recognizing back acupoints, uniquely integrating the traditional TCM bone-measuring principle with advanced deep learning for medical image analysis. The method employs an HRFormer backbone network combined with a Structure-Guided Keypoint Estimation Module (SG-KEM) and a structure-constrained loss function, ensuring anatomically consistent predictions within a standardized spatial coordinate system to improve accuracy across diverse body types. Trained and evaluated on a dataset of 430 high-resolution back images with 19 annotated acupoints, the framework achieved a normalized mean error (NME) of 0.6%, a failure rate (FR@1 cm) of 1.2%, an area under the curve (AUC) of 0.97, and a precision of 93.8%, while operating in real-time at 18 frames per second. Component analysis confirmed significant contributions: the SG-KEM module reduced the mean error by 33.3%, and the structure-constrained loss further decreased it to 0.6%. Moreover, ablation studies under challenging conditions validated the model's robustness. On the obese subset, the NME decreased from 1.5% to 0.8%, FR@1 cm dropped from 4.0% to 1.3%, and precision improved from 83.8% to 93.4%. Under illumination variation, the model achieved an NME of 0.9%, outperforming both HRFormer (1.3%) and HRFormer+SG-KEM (1.1%), with corresponding increases in AUC and precision. These findings demonstrate strong generalization across diverse clinical scenarios. Collectively, these results establish a clinically viable and computationally efficient solution for intelligent acupoint localization, supporting AI-assisted diagnosis and personalized treatment strategies within modern TCM healthcare systems.
准确的穴位定位对于针灸及相关中医治疗的有效性至关重要。本研究引入了一种新颖的自动框架来识别背部穴位，独特地将传统中医骨度分寸法与先进的深度学习用于医学图像分析相结合。该方法采用HRFormer骨干网络，结合结构引导关键点估计模块（SG-KEM）和结构约束损失函数，确保在标准化空间坐标系内进行解剖学上一致的预测，以提高不同体型的准确性。在一个包含430张高分辨率背部图像和19个标注穴位的数据集上进行训练和评估，该框架实现了0.6%的归一化平均误差（NME）、1.2%的失败率（FR@1 cm）、0.97的曲线下面积（AUC）和93.8%的精度，同时以每秒18帧的速度实时运行。组件分析证实了显著贡献：SG-KEM模块将平均误差降低了33.3%，结构约束损失进一步将其降至0.6%。此外，在具有挑战性条件下的消融研究验证了模型的稳健性。在肥胖子集中，NME从1.5%降至0.8%，FR@1 cm从4.0%降至1.3%，精度从83.8%提高到93.4%。在光照变化下，该模型实现了0.9%的NME，优于HRFormer（1.3%）和HRFormer+SG-KEM（1.1%），同时AUC和精度相应提高。这些发现表明在不同临床场景中具有很强的泛化能力。总体而言，这些结果为智能穴位定位建立了一种临床可行且计算高效的解决方案，支持现代中医医疗系统中的人工智能辅助诊断和个性化治疗策略。
