在机器人手术中基于人工智能的废物检测:计算机视觉技术的试点验证研究
Alexis Sanchez1, Luis Herrera2, Andre Teixeira2
1Orlando Health Advanced Robotic Surgery Center, Orlando, FL, USA. Alexis.Sanchez@OrlandoHealth.com.
Journal of robotic surgery
|February 11, 2026
概括
一个新的计算机视觉系统准确地跟踪机器人接器负载浪费,提供客观数据以降低成本. 这项技术提高了机器人手术的成本效益,改善了患者获得先进手术的机会.
科学领域:
- 手术技术 手术技术
- 计算机视觉 计算机视觉
- 医疗保健经济学 医疗保健经济学
背景情况:
- 手术室的废物,特别是机器人手术中的一次性用品的废物,是被忽视的重大成本.
- 目前的废物追踪方法依赖于手动观察,这是低效的,不一致的,缺乏可扩展性.
研究的目的:
- 开发和验证基于计算机视觉的系统 (AIMs),用于自动检测和量化机器人接器负载废物.
- 评估AIMs系统在现实外科环境中的准确性和工作流程集成.
主要方法:
- 一项前性验证研究涉及18个机器人手术在一个大批量中心.
- 利用卷积神经网络从连续的后台视频录制中实时检测对象.
- 将系统生成的日志与并发的手动观测进行比较,在关键时间点评估灵敏度和精度.
主要成果:
- 该AIMs系统实现了完美的 (100%) 灵敏度和精度,用于初始负载检测和最终废物量化.
- 过程中期跟踪显示了增加和删除事件的高灵敏度 (87.7%97.1%) 和完美 (100%) 精度.
- 所有错误都是错误的负值;没有记录错误的正值. 系统运行没有扰乱工作流程,也不需要额外的员工.
结论:
- 基于计算机视觉的AIMs系统提供准确,可扩展和客观的数据,用于管理外科用品废弃物.
- 这项技术支持机器人手术程序中的偏好卡优化和成本降低计划.
- 提高成本效益可以促进更广泛的患者获得先进的机器人外科技术.
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