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.

这个小企业创新研究（SBIR）第二阶段项目的更广泛的影响/商业潜力是改善手术技能的获取，手术性能的评估和医疗设备培训。以学徒为基础的外科培训模式造成了医疗设备和医疗保健行业的低效率。外科手术和设备的复杂性和专业化加剧了这一问题。该技术结合了逼真的物理模拟过程、新颖的传感技术和机器学习数据分析，以满足市场对数据驱动培训的普遍需求。在该项目中开发的技术将产生手术模拟平台，以提高手术能力和在手术室外实践设备部署的能力，同时为手术性能和定量评估提供关键的数据驱动洞察。最终，该解决方案可以降低患者成本，改善结果，并加快医疗设备的开发和采用。拟议的项目将导致一个综合系统的发展，收集数据和评估血管外科手术在开放和血管内领域的表现。先前开发的用于培训外科医生的开放式血管手术模拟平台将扩展到包括血管内手术和电容传感器的集成，以捕获一组全面的手术性能数据。本项目旨在利用人工智能对收集到的数据集中的关键绩效指标进行分类，建立一个综合的模型来对操作绩效进行分类。外科培训和医疗设备开发的数据驱动平台目前还没有商业化，而且该行业目前依赖于成本越来越高的手段来提供重要的外科培训。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。