I-Corps: Laparotomy simulator with mechanical feedback for surgical training

I-Corps项目更广泛的影响/商业潜力是开发一个培训外科住院医师实践和改进剖腹手术关键缝合技术的平台。这有望提高住院医生在模拟训练期间的学习，这可能转化为提高知识和肌肉记忆。学习经验可以提高外科住院医师的知识、技能和准备，从而改善临床结果。预期的结果包括腹部关闭后并发症的最小化和更好的病人健康和满意度，因为更多准备充分的医生进入实践。该技术的有效开发和部署可以显著降低培训外科医生进行高级外科手术所需的障碍，并改善医疗保健培训和实践。这个I-Corps项目是基于外科训练模拟软件平台的开发。提出的人在环学习方法是一种外科剖腹手术模拟，具有机械反馈，并由经验丰富的外科医生指导，适合有效的外科训练。该装置可以更好地理解缝合过程中的人为错误如何影响缝合生物材料中的力分布，以及哪种缝合策略在机械上是最理想的。此外，它还可用于使学习方法与处于不同培训阶段的受训者的能力相匹配。所提出的技术也可用于模拟来自不同背景的患者，这可能会导致了解力学和几何的差异如何在确定最佳外科手术技术时提出挑战。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 platform for training surgical residents to practice and improve critical suturing techniques for laparotomy. This is expected to enhance resident learning during simulation training, which may translate to improved knowledge and muscle memory. The learning experience may improve knowledge, skills, and readiness for surgical residents, which may result in improved clinical outcomes. Expected outcomes include the minimization of complications following abdominal closing and better patient well-being and satisfaction as more well-prepared physicians enter practice. Effective development and deployment of this technology may significantly lower the barrier required to train surgeons for advanced surgical procedures, and result in an improvement in healthcare training and practices. This I-Corps project is based on the development of a simulation software platform for surgical training. The proposed human-in-the-loop learning approach is a surgical laparotomy simulation with mechanical feedback additionally supported with instruction by an experienced surgeon suitable to effective surgical training. The device may lead to a better understanding of how human errors in suturing affects force distributions in sutured biomaterials, and which strategies for suturing are the most optimal mechanically. In addition, it may be used to match the learning approach to the capabilities of the trainees that are at different training stages. The proposed technology also may be used to simulate patients from diverse backgrounds, which may lead to knowledge of how variances of mechanics and geometry presents challenges in determining the best techniques for surgical operations.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.