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开发外科医生机器接口:使用一个新的实例细分框架进行手术内标记里程碑标签.

Jay J Park1,2, Nehal Doiphode1,3, Xiao Zhang4

  • 1Department of Neurosurgery, The Surgical Innovation and Machine Interfacing (SIMI) Lab, Stanford University School of Medicine, Stanford, CA, United States.

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概括
此摘要是机器生成的。

这项研究引入了一种新的外科医生机器接口 (SMI),使用人工智能 (AI) 提高手术精度. 人工智能平台显著提高了在手术期间识别解剖学里程碑的准确性和速度,提升了患者的安全性和培训.

关键词:
动脉静脉状囊 动脉静脉状囊人工智能的人工智能是人工智能.全球神经外科全球神经外科在手术内提供指导.机器学习是机器学习.脊柱 脊柱 脊柱 脊柱 脊柱外科医生机器界面的使用手术指南手术指南

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科学领域:

  • * 医学人工智能 * 医学人工智能
  • * 手术计算机视觉 * 手术计算机视觉
  • * 神经外科技术 * 神经外科技术

背景情况:

  • *人工智能 (AI) 提高了外科手术的安全性,培训和患者的治疗结果.
  • * 外科医生机器接口 (SMI) 代表了外科医生和机器推理的交叉点.
  • * 计算机视觉 (CV) 在外科手术中的应用正在迅速发展.

研究的目的:

  • *为SMI开发和评估一个定制的深度CV架构.
  • * 在持续性动脉静脉 (dAVF) 手术视频中执行解剖学标志和手术工具的实例细分.
  • * 用最先进的细分模型来评估SMI的表现.

主要方法:

  • * 基于SOLOv2架构的定制深卷积神经网络被开发出来.
  • *使用的标签很少,在8520个测试中,只有133个注释用于培训.
  • *使用F1得分和平均精度 (mAP) 评估性能,与MaskRCNN,YOLOv3和SOLOv2.2进行比较.

主要成果:

  • * SMI实现了卓越的准确性和速度,F1得分为44.2%,mAP为46.3% (在IOU>50%时).
  • *推断时间最快88ms,超过了其他测试的架构.
  • * 该模型表现出良好的概括性,识别了训练集中不存在的对象.

结论:

  • * 开发的SMI平台显示了对手术内指导的重大前景.
  • *高样本效率和优于现有模型的性能突显了它的潜力.
  • *未来的工作包括用于更广泛的外科应用和实时指导能力的转移学习.