STTR Phase I: Computerized System for Detection, Assessment, and Visualization of Intraoperative Bleeding During Robotic and Laparoscopic Surgery

该小企业技术转让（STTR）I期项目的更广泛影响/商业潜力是降低接受手术的患者的发病率和死亡率。 手术期间意外出血是全世界数百万患者在所有类型的手术中发生的关键问题。 该项目将推进使用人工智能（AI）的机器人手术工具系统，以管理术中出血。 减少失血将导致输血需求减少，医疗保健成本降低，并改善手术后的恢复。 这项技术将推动螺栓固定式安全实用工具的通用标准进入不断发展的手术工具制造商市场。小企业技术转让（STTR）第一阶段项目将推动智能术中系统的转化。 目前，还没有工具来检测或表征出血，因此外科医生必须持续监控摄像机视图以发现出血并估计出血源，出血源通常淹没在血泊中。 拟议的努力将推进一个原型，以协助外科医生在检测，可视化，并在真实的时间在泌尿外科手术动脉出血的特点。出血源将通过2D或3D（增强现实）叠加呈现给外科医生，使他/她能够精确快速地控制出血。 该技术以一种新颖的方式融合了机器人技术、计算机视觉和机器学习，生产出一种手术工具，将大大推进当前的最先进技术。该奖项反映了NSF的法定使命，并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，被认为值得支持。

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to reduce the morbidity and mortality of patients undergoing surgery. Inadvertent bleeding during surgery represents a critical problem that occurs during all types of procedures on millions of patients around the world. This project will advance a robotic surgical tool system using artificial intelligence (AI) to manage intraoperative bleeding. Reduced blood loss will lead to a reduced demand for blood transfusions, reduced healthcare costs, and improved recuperation from surgery. This technology will advance a universal standard for bolt-on safety utilities to the evolving surgical tool manufacturer market.The Small Business Technology Transfer (STTR) Phase I project will advance the translation of an intelligent intraoperative system. Currently, there is no tool to detect or characterize bleeding, so the surgeon must continually monitor the camera view for bleeding and estimate the source of the bleed, which is often submerged in a pool of blood. The proposed effort will advance a prototype to assist a surgeon in detecting, visualizing, and characterizing arterial bleeding in real time during urological surgery. The source of the bleeding will be presented to the surgeon using 2D or 3D (augmented reality) overlays, enabling him/her to control the bleeding precisely and quickly. The technology fuses robotics, computer vision, and machine learning in a novel manner to produce a surgical tool that will significantly advance the current state-of-the-art.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.