MAESTRO Jr. - Multi-sensing AI Environment for Surgical Task & Role Optimisation

MAESTRO Jr. - 用于手术任务的多传感人工智能环境

• 批准号: EP/W004755/1
• 负责人: George Mylonas
• 金额: $ 38.41万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW004755%2F1
• 关键词: MAESTRO; Jr.; Multi; sensing; AI

This project is about devising and implementing a smart operating room environment, powered by trustable, human-understanding artificial intelligence, able to continually adapt and learn the best way to optimise safety, efficacy, teamwork, economy, and clinical outcomes. We call this concept MAESTRO. A fitting analogy for MAESTRO is that of an orchestra conductor, a 'maestro', who oversees, overhears and directs a group of people on a common task, towards a common goal: a masterful musical performance. Although the music score is identical for all orchestras, there is no doubt that they all perform it in different ways and some significantly better than others. Although the quality and personality of orchestra musicians is very important, it is widely accepted that the role of the maestro is crucial, and extends beyond the duration of the musical performance to rehearsals and understanding of the context behind the music score. Thus, while it is possible for orchestras to perform without conductors, most cannot function without one.Our proposed MAESTRO AI-powered operating room of the future rotates around four key elements:(a) The holistic sensing of patient, staff, operating room environment and equipment through an array of diverse sensor devices.(b) Artificial intelligence focused on humans (human-centric), able to continually understand situations and actions developing in the operating room, and of intervening when necessary.(c) The use of advanced human-machine user interfaces for augmenting task performance.(d) A secure device interconnectivity platform, allowing the full integration of all above key elements.As in our orchestra analogy, our envisioned MAESTRO directs the OR staff and surgical devices before, during and after a surgical procedure by:(1) Sensing surgical procedures in all their aspects, including those which are currently neglected such as the physiological responses of staff (e.g., heart rate, blood pressure, sweating, pupil dilation), focus of attention, brain activity, as well as harmful events that may escape the attention of the clinical team.(2) Overseeing individual and team performance in real-time, throughout the operation and across different types of surgeries and different teams.(3) Guiding and assisting the surgical team via automated checkpoints, virtual and augmented visualisations, warnings, individualised and broadcasted alerts, automation, semi-automation, robotics, and other aids and factors that can affect performance in the operating room.(4) Augmenting and optimising individual and collective operational capabilities, skills, and task ergonomics, through novel human-machine interaction and interfacing modalities.The project is designed to have a significant societal, economic and technological impact, and to establish the NHS as a leading healthcare paradigm worldwide. MAESTRO leverages the expertise of top researchers in the areas of robotics, sensing, artificial intelligence, human factors, health policies and patient safety. It is co-designed in collaboration with top clinicians, one of the largest NHS Trusts in England, patient groups, performing artists, and several small and medium-sized enterprises and large multinational industries operating in the areas of artificial intelligence, medical devices, digital health, large networks, cloud services, cyber security.

该项目旨在设计和实施智能手术室环境，该环境由可信赖的、人类理解的人工智能提供支持，能够不断适应和学习优化安全性、有效性、团队合作、经济性和临床结果的最佳方法。我们称这个概念为MAESTRO。 MAESTRO 的一个恰当的类比是管弦乐队指挥，即“大师”，他监督、偷听并指导一群人完成一项共同任务，实现一个共同目标：精湛的音乐表演。尽管所有管弦乐队的乐谱都是相同的，但毫无疑问，他们都以不同的方式演奏，并且有些管弦乐队的表现明显优于其他管弦乐队。尽管乐团音乐家的素质和个性非常重要，但人们普遍认为指挥家的角色至关重要，并且超越了音乐表演的持续时间，延伸到排练和对乐谱背后背景的理解。因此，虽然管弦乐队可以在没有指挥的情况下进行表演，但大多数乐队在没有指挥的情况下都无法运作。我们提出的 MAESTRO 人工智能驱动的未来手术室围绕四个关键要素：(a) 对患者、工作人员、手术室环境的整体感知(b) 人工智能以人为中心（以人为中心），能够持续了解手术室中发生的情况和行动，并在必要时进行干预。(c) 使用先进的用于增强任务的人机用户界面(d) 安全的设备互连平台，允许完全集成上述所有关键要素。就像我们的管弦乐队类比一样，我们设想的 MAESTRO 在外科手术之前、期间和之后通过以下方式指导手术室工作人员和手术设备：(1)感知手术过程的各个方面，包括目前被忽视的方面，例如工作人员的生理反应（例如心率、血压、出汗、瞳孔扩张）、注意力集中、大脑活动以及可能发生的有害事件逃避注意(2) 在整个手术过程中以及不同类型的手术和不同团队中实时监督个人和团队的表现。(3) 通过自动检查点、虚拟和增强可视化、警告来指导和协助手术团队、个性化和广播警报、自动化、半自动化、机器人技术以及其他可能影响手术室表现的辅助设备和因素。(4) 通过新颖的方式增强和优化个人和集体的操作能力、技能和任务人体工程学人机交互和接口模式。该项目旨在产生重大的社会、经济和技术影响，并将 NHS 打造为全球领先的医疗保健范例。 MAESTRO 利用机器人、传感、人工智能、人为因素、健康政策和患者安全领域顶尖研究人员的专业知识。它是与顶级临床医生、英国最大的 NHS 信托机构之一、患者团体、表演艺术家以及在人工智能、医疗设备、数字化领域运营的多家中小企业和大型跨国企业共同设计的。健康、大型网络、云服务、网络安全。

项目成果

The Measurement of Cognitive Workload in Surgery Using Pupil Metrics: A Systematic Review and Narrative Analysis.

使用瞳孔指标测量手术中的认知工作量：系统回顾和叙述分析。

• DOI: http://dx.10.1016/j.jss.2022.07.010
• 发表时间: 2022
• 期刊: The Journal of surgical research
• 作者: Naik R

Preliminary findings of a multimodal sensor system for measuring surgeon cognitive workload

用于测量外科医生认知工作量的多模态传感器系统的初步发现

• DOI: http://dx.10.31256/hsmr2022.66
• 发表时间: 2022
• 作者: Naik R

Machine learning for technical skill assessment in surgery: a systematic review

机器学习在外科技术技能评估中的应用：系统评价

• DOI: 10.1038/s41746-022-00566-0
• 发表时间: 2022-03-03
• 期刊: NPJ Digital Medicine
• 影响因子: 15.2
• 作者: Lam K;Chen J;Wang Z;Iqbal FM;Darzi A;Lo B;Purkayastha S;Kinross JM
• 通讯作者: Kinross JM