I-Corps: Artificial Intelligence and Haptic-Enabled Robotic Assistant for Surgeons

I-Corps项目更广泛的影响/商业潜力是开发一种可穿戴系统，该系统能够感知和分析外科医生和外科学员的运动数据，以提供在线触觉指导反馈，以提高手术效果。机器人手术仍然是一项复杂且要求很高的精神运动任务，需要大量的训练才能掌握。此外，外科劳动力的日益短缺增加了提供全面培训的挑战。这项拟议中的技术旨在帮助外科医生提高他们的手术技能。这项技术在农村地区或受外科医生劳动力短缺影响的地区尤其有用，因为获得训练有素的外科医生的机会有限。目标是通过开发在线分析和触觉反馈策略，降低外科医生学习和使用手术机器人的障碍，从而使外科医生更容易、更安全地学习和使用手术机器人，最终改善患者的治疗效果。I-Corps项目基于人工智能（AI）和触觉可穿戴技术的发展，以协助分析手术动作并提供近乎实时的手术技能指导。提出的技术使用以人为中心的数据驱动模型进行手术动作分析和上下文感知。这些模型旨在检测一系列行为，包括风格行为、专业水平、双手协调以及外科医生在机器人手术和训练期间的压力水平。这些模型利用机器学习、深度学习和数学方法来实时分析手术机器人及其操作员的运动学数据，使它们能够使用视觉和触觉技术向外科医生提供即时反馈和指导，从而提高他们的手术性能。这项技术有可能大大减少有效操作手术机器人所需的培训，使外科医生更容易将这项技术整合到他们的实践中。此外，该技术有可能通过将手术错误和并发症的风险降至最低来改善患者的预后。系统提供的实时反馈和指导使外科医生能够在手术过程中做出更准确、更明智的决定，减少错误和并发症的可能性。这有可能提高患者的安全性，减少术后干预的需要，最终导致更好的临床结果。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of a wearable system capable of sensing and analyzing motion data of surgeons and surgical trainees to provide online, haptic guidance feedback on how to improve surgical performance. Robotic surgery remains a complex and demanding psychomotor task that requires extensive training to master. In addition, a growing shortage within the surgical workforce adds to the challenge of providing comprehensive training. The proposed technology is designed to help surgeons improve their surgical skills. This technology may be particularly useful in rural areas or those impacted by surgeon workforce shortages as access to well-trained surgeon experts is limited. The goal is to make it easier and safer for surgeons to learn and use surgical robots by lowering the barrier for surgeons to learn and use surgical robots through developing online analytics and haptic feedback strategies, ultimately improving patient outcomes.This I-Corps project is based on the development of artificial intelligence (AI) and haptic enabled wearable technology to assist with analyzing surgical motions and providing surgical skill guidance in near real-time. The proposed technology uses human-centered, data driven models for surgical movement analysis and context awareness. These models are designed to detect a range of behaviors, including stylistic behaviors, expertise levels, bimanual coordination, and stress levels of surgeons during robotic surgery and training. The models leverage machine learning, deep learning, and mathematical methods to analyze kinematic data from surgical robots and their operators in real time, making them unique in their ability to provide immediate feedback and guidance using visual and haptic technologies to surgeons, augmenting their surgical performance. This technology has the potential to greatly reduce the training required to operate surgical robots effectively, making it easier for surgeons to integrate this technique into their practice. In addition, the proposed technology has the potential to improve patient outcomes by minimizing the risk of surgical errors and complications. The real-time feedback and guidance provided by the system enable surgeons to make more accurate and informed decisions during surgeries, reducing the likelihood of errors and complications. This has the potential to improve patient safety and reduce the need for postoperative interventions, ultimately leading to better clinical outcomes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.