SBIR Phase II: Quantification of Operative Performance via Simulated Surgery, Capacitive Sensing, and Machine Learning to Improve Surgeon Performance & Medical Device Develop

SBIR 第二阶段：通过模拟手术、电容传感和机器学习量化手术表现，以提高外科医生的表现

• 批准号: 2223976
• 负责人: Hannah Eherenfeldt
• 金额: $ 99.64万
• 依托单位: Resuture Inc.
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223976&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Quantification; Operative

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to improve surgical skill acquisition, assessment of surgical performance, and medical device training. The apprenticeship-based model of surgical training has created inefficiencies in the medical device and healthcare industries. This problem is exacerbated by the evolving complexity and specialization of surgical procedures and devices. The proposed technology combines lifelike, physical simulated procedures, novel sensing technologies, and machine-learned data analytics to address a universal market need for data-driven training. The technology developed during this project will result in surgical simulation platforms to improve procedural competency and the ability to practice device deployment outside of the operating room, while providing critical data-driven insight into surgical performance and quantitative evaluation. Ultimately, this solution could reduce patient costs, improve outcomes, and expedite medical device development and adoption. The proposed project will result in the development of a comprehensive system that collects data and evaluates vascular surgical operative performance in both the open and endovascular fields. An open vascular surgery simulation platform previously developed to train surgeons will be expanded to include endovascular procedures and the integration of capacitive sensors to capture a comprehensive set of operative performance data. This project aims to use artificial intelligence to classify key performance metrics from the collected dataset to build a comprehensive model to classify operative performance. A data-driven platform for surgical training and medical device development is not currently commercially available and the industry currently relies on increasingly cost-prohibitive means to provide vital surgical training.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.

这项小型企业创新研究（SBIR）II期项目的更广泛的影响/商业潜力是改善手术技能获取，手术性能评估和医疗设备培训。基于学徒的手术培训模型在医疗设备和医疗保健行业中创造了低效率。外科手术和设备的复杂性和专业化的不断发展而加剧了这个问题。提出的技术结合了寿命，物理模拟程序，新型的传感技术和机器学习数据分析，以满足数据驱动培训的通用市场需求。该项目期间开发的技术将导致手术模拟平台，以提高程序能力和在手术室外练习设备部署的能力，同时为手术性能和定量评估提供关键的数据驱动洞察力。最终，该解决方案可以降低患者成本，改善结果并加快医疗设备开发和采用。拟议的项目将导致一个综合系统的发展，该系统收集数据并评估开放和血管内领域的血管手术手术性能。先前开发的训练外科医生的开放血管外科手术模拟平台将扩展，包括血管内手术和电容传感器的整合，以捕获一系列全面的手术性能数据。该项目旨在使用人工智能对收集的数据集进行关键绩效指标进行分类，以建立一个综合模型来对操作性绩效进行分类。目前尚未在商业上获得数据驱动的手术培训和医疗设备开发的平台，目前该行业依靠越来越多的成本良好的手段来提供重要的手术培训。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来通过评估来支持的。