互惠:在手术室实现整体感知和推理
Julien Quarez1, Yang Li1, Hassna Irzan1
1Surgical & Interventional Engineering, King's College London, UK.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|January 27, 2025
概括
我们开发了MUTUAL和MUTUAL-ROS 2,这些数据平台用于体内AI (E-AI) 在外科手术中. 互联在录制方面表现出色,而MUTUAL-ROS 2则为手术室的AI推理提供了卓越的实时流媒体.
科学领域:
- 手术机器人技术和智能系统
- 人工智能发展的数据基础设施.
- 医疗信息学 医疗信息学
背景情况:
- 嵌入式人工智能 (E-AI),包括手术机器人,需要专门的数据平台进行开发和部署.
- 现有的数据解决方案可能无法充分满足临床环境中实时人工智能处理的复杂需求.
研究的目的:
- 介绍和评估MUTUAL,一个跨平台的多式联络数据记录和流媒体软件.
- 引入和评估MUTUAL-ROS 2,一个针对E-AI系统的分布式适应.
- 为了比较两个系统的性能和适合性的外科数据管理.
主要方法:
- 在两项临床研究中部署MUTUAL和MUTUAL-ROS 2.
- 在不同的设置和模式 (视频,音频,专家评论) 中对录制性能进行比较分析.
- 对实时流媒体能力和AI推断可扩展性的评估.
主要成果:
- 互动展示了强大的记录性能,适合数据策划和离线分析.
- 互联-ROS 2 显示了 E-AI 在手术室实时流媒体和水平可扩展性的优势.
- 对于分布式,边缘处理的模式,MUTUAL-ROS 2的记录可靠性需要进一步提高.
结论:
- 互惠是全面数据收集和术后分析的理想选择.
- 在手术中,MUTUAL-ROS 2更适合实时AI推断和可扩展的E-AI应用.
- 在分布式环境中优化MUTUAL-ROS 2以记录可靠性对于推进外科E-AI至关重要.
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