人工智能辅助的外科视觉:评估YOLOv8和YOLOv12在结肠癌手术中的实时检测
Li Li1,2, Bin Xuan1,2, Xin Song3
1Department of Surgery, Hebei Medical University, Shijiazhuang, Hebei, China.
Frontiers in surgery
|January 29, 2026
概括
与YOLOv8.8相比,YOLOv12在胃肠道手术中的对象检测和实例细分方面显示出更好的回忆能力. 这种人工智能模型显示了提高机器人结肠癌手术效率和安全性的潜力,特别是在有限的硬件的情况下.
科学领域:
- 医疗人工智能 医疗人工智能
- 手术技术 手术技术
- 计算机视觉在外科手术中的应用
背景情况:
- 由于组织变形,目前的手术内导航系统在胃肠道手术中受到限制.
- 实时适应对于在动态的外科环境中提高程序效率和安全至关重要.
研究的目的:
- 评估和比较YOLOv8和YOLOv12模型在动态胃肠道手术场景中的实时对象检测和实例细分的性能.
- 确定一个最佳的人工智能模型,以提高外科导航和安全性.
主要方法:
- 一项多中心的回顾性研究,涉及22个手术视频中的1847张图像.
- 培训和验证用于对象检测和实例分割的YOLOv8和YOLOv12模型.
- 使用精度,回忆,mAP@0.5,mAP@0.5-0.95和重量文件大小等指标进行性能比较.
- 通过问卷调查进行临床适用性评估.
主要成果:
- 无论是YOLOv8还是YOLOv12,都表现出了良好的性能.
- 在对象检测 (P=0.037) 和实例分割 (P=0.031) 方面,YOLOv12的回忆率明显高于YOLOv8.
- 在mAP@0.5 (P=0.045) 和回忆 (P=0.036) 的测试集中,YOLOv12对象检测的表现优于实例细分.
- 与YOLOv8 (6.8 MB) 相比,YOLOv12具有较小的重量文件大小 (6.0 MB).
结论:
- 建议YOLOv12用于机器人结肠癌手术中的物体检测,特别是在资源有限的环境中,以提高效率和安全性.
- 人工智能辅助技术显示了减少手术时间和错过淋巴结检测的潜力,特别是对于初级外科医生来说.
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