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Background <p>Vacuum-assisted breast biopsy (VABB) is a widely adopted minimally invasive technique for the diagnosis and treatment of breast lesions. However, the procedure heavily relies on real-time ultrasound guidance, posing significant challenges for junior surgeon who lack radiological experience in accurately localizing both the lesion and the biopsy needle. Currently, there is no dedicated real-time artificital intelligence (AI) navigation system specifically designed for VABB procedures.</p> Methods <p>We developed a novel two-stage real-time AI navigation system based on the YOLOv11 deep learning architecture. Model 1 performs initial localization of the tumor and the cutter slot, while Model 2 focuses on real-time tracking of the knife and tumor during resection. The system was trained and validated using 22,278 annotated ultrasound images from 167 VABB procedures conducted at People's Hospital of China Medical University. A rigorous three-fold cross-validation was implemented to assess model performance, and the localization accuracy was compared with that of junior surgeon. Additionally, we evaluated the system’s real-time processing performance on both GPU and CPU platforms.</p> Results <p>The AI system demonstrated superior performance across all evaluated metrics. ForModel 1, the mean Average Precision at IoU threshold 0.5 (mAP50) for tumor detection and groove localization reached 0.907 and 0.671, respectively, significantly outperforming junior surgeon (0.551 and 0.120). For Model 2, the mAP50 for tumor and needle tip tracking were 0.829 and 0.765, respectively, compared to 0.758 and 0.350 achieved by surgeons. The system achieved a real-time processing speed of 1.2 ms per frame on GPU and 32.6 ms per frame on CPU.</p> Conclusion <p>This study presents the first dedicated AI-based navigation system for VABB, showing substantial improvement in localization accuracy over manual operation by junior surgeon. The system’s robust detection capability and real-time performance highlight its strong potential for clinical application, especially in surgical training and complex cases requiring precise instrument control.</p> Trial registration <p>Clinical Trial Registration No. 2022JH2/101300026</p>

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Real-time deep learning for tumor segmentation and tool tracking: development and validation of an AI navigation system in vacuum-assisted breast biopsy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月13日

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  • Xinran Shao,
  • Yunzhi Shen,
  • Pingdong Sun,
  • Yihan Sun,
  • Xingai Ju,
  • Hongjie Zhu,
  • Hong Li,
  • Qiushi Li,
  • Ruan Ting,
  • Jinrui Liu,
  • Yuqing Wang,
  • Qikun Guo,
  • Yuxin Ma,
  • Xiang Fei,
  • Hang Sun,
  • Jianchun Cui

摘要

Background

Vacuum-assisted breast biopsy (VABB) is a widely adopted minimally invasive technique for the diagnosis and treatment of breast lesions. However, the procedure heavily relies on real-time ultrasound guidance, posing significant challenges for junior surgeon who lack radiological experience in accurately localizing both the lesion and the biopsy needle. Currently, there is no dedicated real-time artificital intelligence (AI) navigation system specifically designed for VABB procedures.

Methods

We developed a novel two-stage real-time AI navigation system based on the YOLOv11 deep learning architecture. Model 1 performs initial localization of the tumor and the cutter slot, while Model 2 focuses on real-time tracking of the knife and tumor during resection. The system was trained and validated using 22,278 annotated ultrasound images from 167 VABB procedures conducted at People's Hospital of China Medical University. A rigorous three-fold cross-validation was implemented to assess model performance, and the localization accuracy was compared with that of junior surgeon. Additionally, we evaluated the system’s real-time processing performance on both GPU and CPU platforms.

Results

The AI system demonstrated superior performance across all evaluated metrics. ForModel 1, the mean Average Precision at IoU threshold 0.5 (mAP50) for tumor detection and groove localization reached 0.907 and 0.671, respectively, significantly outperforming junior surgeon (0.551 and 0.120). For Model 2, the mAP50 for tumor and needle tip tracking were 0.829 and 0.765, respectively, compared to 0.758 and 0.350 achieved by surgeons. The system achieved a real-time processing speed of 1.2 ms per frame on GPU and 32.6 ms per frame on CPU.

Conclusion

This study presents the first dedicated AI-based navigation system for VABB, showing substantial improvement in localization accuracy over manual operation by junior surgeon. The system’s robust detection capability and real-time performance highlight its strong potential for clinical application, especially in surgical training and complex cases requiring precise instrument control.

Trial registration

Clinical Trial Registration No. 2022JH2/101300026