Structure-Guided Deep Learning for Back Acupoint Localization via Bone-Measuring Constraints

Yulong Wang¹Tian Lan²Wenjian Dou³Zhi Chen⁴Song Zhang³Gong Chen^1,3*

• ¹School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, China
• ²School of Computer Science, Inner Mongolia University, China
• ³Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, China
• ⁴Institute of Chinese Medicine Literature, Nanjing University of Chinese Medicine, Nanjing, China

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 structureconstrained 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@1cm) 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@1cm 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.