实时深度学习用于瘤细分和工具跟踪:在真空辅助乳腺活检中开发和验证AI导航系统
Xinran Shao1, Yunzhi Shen1,2, Pingdong Sun1
1Department of Thyroid and Breast Surgery, People's Hospital of China Medical University(People's Hospital of Liaoning Pronvince), Shenyang, China.
World journal of surgical oncology
|November 12, 2025
概括
这项研究引入了一种人工智能导航系统,可以显著提高真空辅助乳腺活检 (VABB) 过程中的定位精度. 人工智能系统的性能优于初级外科医生,提高了乳腺损伤诊断和治疗的安全性和精度.
科学领域:
- 医疗成像医学成像
- 在外科手术中使用人工智能
- 手术导航 手术导航
背景情况:
- 真空辅助乳腺活检 (VABB) 是乳腺病变的关键最小侵入性技术.
- 目前的VABB依赖于超声波指导,挑战经验较少的外科医生在精确的病变和针定位.
- 目前对于VABB.没有专门的实时AI导航系统.
研究的目的:
- 为VABB.开发和评估一种新的两阶段实时AI导航系统.
- 在VABB手术过程中提高病变和仪器定位的准确性和效率.
- 与初级外科医生相比,评估系统的性能及其实时处理能力.
主要方法:
- 使用YOLOv11深度学习架构开发了一个两级AI导航系统.
- 模型1:瘤和切割槽的初始定位. 模型2:刀和瘤的实时跟踪.
- 在167个VABB程序中的22278张超声图像上接受了培训和验证;采用了三重交叉验证.
主要成果:
- 人工智能系统在定位准确性方面明显优于初级外科医生 (例如,mAP50用于瘤检测:0.907比0.551).
- 模型2在瘤和针头跟踪方面实现了高mAP50 (0.829和0.765分别).
- 实时处理速度:1.2 ms/frame (GPU) 和32.6 ms/frame (CPU). 实时处理速度:1.2 ms/frame (GPU) 和32.6 ms/frame (CPU). 实时处理速度:1.6 ms/frame (GPU) 和32.6 ms/frame (CPU). 实时处理速度:1.6 ms/frame (GPU) 和32.6 ms/frame (CPU). 实时处理速度:1.2 ms/frame (GPU) 和32.6 ms/frame (CPU).
结论:
- 介绍了首个专门针对VABB的基于AI的导航系统.
- 与手动方法相比,在本地化准确度方面取得了实质性的改进.
- 突出了在外科培训和复杂的VABB病例中临床应用的潜力.
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