Lin Shun-Ku, Su Chien-Kun, Mercado Melnard Rome C, Peng Syu-Jyun
Department of Chinese medicine, Renai Branch, Taipei City Hospital, Taipei, Taiwan.
Institute of Traditional Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
BMC Complement Med Ther. 2025 Mar 18;25(1):108. doi: 10.1186/s12906-025-04853-7.
Acupuncture is a widely practiced traditional therapy, yet safety concerns, particularly needle breakage and retention, remain critical issues that can lead to complications such as infections, organ injury, or chronic pain. This study aimed to develop a deep learning model to monitor acupuncture needle insertion, detect instances of needle breakage, and prevent needle retention, ultimately improving patient safety and treatment outcomes.
A deep learning model based on the YOLOv8 architecture was trained using a dataset comprising 192 images from a commercial image library and 73 clinical images captured during real-world acupuncture sessions. Images were preprocessed through cropping and annotation, and augmented to enhance model generalizability. Five-fold cross-validation was employed to ensure robust performance. Model evaluation metrics included precision, recall, F1 score, and mean average precision (mAP) at Intersection over Union (IoU) thresholds of 50% (mAP@50) and 50-95% (mAP@50-95).
The model demonstrated strong performance, achieving an average precision of 88.0% and a recall of 82.9%. The mean average precision was 88.6% at mAP@50 and 62.9% at mAP@50-95, indicating high reliability in detecting acupuncture needles across diverse scenarios. These results highlight the potential of the model to enhance clinical safety by minimizing risks associated with needle breakage and retention, regardless of practitioner experience or patient demographics.
The proposed YOLOv8-based deep learning model offers a reliable method for real-time needle monitoring in acupuncture. Its integration into clinical workflows can improve safety and efficiency, especially in underserved regions or settings with less experienced practitioners. Future research should validate the model with larger, more diverse datasets and explore its application in various healthcare settings.
Not applicable; this study did not involve a healthcare intervention requiring registration. Data collection adhered to ethical standards with institutional approval (TCHIRB-11310004).
针灸是一种广泛应用的传统疗法，但安全问题，尤其是针具折断和针具滞留，仍然是可能导致感染、器官损伤或慢性疼痛等并发症的关键问题。本研究旨在开发一种深度学习模型，以监测针灸针的插入过程，检测针具折断情况，并防止针具滞留，最终提高患者安全性和治疗效果。
基于YOLOv8架构的深度学习模型使用一个数据集进行训练，该数据集包括来自商业图像库的192张图像和在实际针灸过程中拍摄的73张临床图像。图像经过裁剪和标注进行预处理，并进行增强以提高模型的泛化能力。采用五折交叉验证来确保稳健的性能。模型评估指标包括精度、召回率、F1分数以及在交并比（IoU）阈值为50%（mAP@50）和50 - 95%（mAP@50 - 95）时的平均精度均值（mAP）。
该模型表现出强大的性能，平均精度达到88.0%，召回率为82.9%。在mAP@50时平均精度均值为88.6%，在mAP@50 - 95时为62.9%，表明在不同场景下检测针灸针具具有高可靠性。这些结果凸显了该模型通过将与针具折断和滞留相关的风险降至最低来提高临床安全性的潜力，无论从业者经验或患者特征如何。
所提出的基于YOLOv8的深度学习模型为针灸中的实时针具监测提供了一种可靠的方法。将其整合到临床工作流程中可以提高安全性和效率，特别是在服务不足的地区或从业者经验较少的环境中。未来的研究应使用更大、更多样化的数据集验证该模型，并探索其在各种医疗环境中的应用。
不适用；本研究不涉及需要注册的医疗干预。数据收集在获得机构批准（TCHIRB - 11310004）的情况下符合伦理标准。
