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

这个I-Corps项目的更广泛的影响/商业潜力是开发一个平台，用于培训外科住院医生练习和改进剖腹手术的关键技术。 预计这将在模拟训练期间加强居民学习，这可能会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，这可能会导致改善临床结果。预期的结果包括最大限度地减少腹部闭合后的并发症，以及随着更多准备充分的医生进入实践，患者的健康和满意度更好。 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先进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项目的基础是开发一个用于外科培训的模拟软件平台。 所提出的人在回路学习方法是具有机械反馈的外科剖腹手术模拟，另外由适合于有效手术训练的经验丰富的外科医生的指令支持。 该设备可能会让人们更好地了解缝合中的人为错误如何影响缝合生物材料的力分布，以及哪些缝合策略在机械上是最佳的。 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。 该技术也可用于模拟来自不同背景的患者，这可能会导致力学和几何形状的差异如何在确定外科手术的最佳技术提出挑战的知识。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.