Structure-guided deep learning for back acupoint localization via bone-measuring constraints

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.