Next Generation of Surgical Imaging and Robotics for Supervised Autonomous Soft Tissue Surgery

 描述(由申请人提供)：吻合术是所有重建手术的必要和关键部分，涉及从心血管到胃肠(GI)手术的任何腔结构。在美国，每年仅内脏指征(胃肠、泌尿外科和妇科手术)就进行了100多万次吻合术。然而，高达30%的胃肠道吻合口合并渗漏、狭窄和狭窄，部分原因是技术和技术问题。吻合口并发症使患者死亡率从三倍到十倍显著增加，并降低了受影响患者的功能和生活质量。虽然微创手术方法改变了外科手术，显著减少了与手术部位相关的侧枝组织损伤，但手术工具和视觉技术的最新进展并未解决影响吻合口结果的关键因素。并发症发生率缺乏改善证明了这一点。相反，当前的微创手术(MIS)或机器人辅助手术(RAS)由于视觉和空间的限制，对吻合术提出了额外的新挑战。这项研究的长期目标是通过机器人执行最好的吻合技术来减少并发症和改善吻合术的功能结果。以下具体目标将推动这项技术的开发，并证明其作为临床采用途径的可行性：目标1：使用多光谱成像确定最佳缝合位置。我们将比较由我们的新算法指导的缝合位置和吻合口结果，这些缝合位置优化算法结合了地下解剖和生理信息，并在临床前研究中由专家外科医生进行。目标2：在非结构化手术环境中准确跟踪可移动和可变形的软组织目标。我们将展示我们基于全光成像和近红外标记技术的创新融合3D跟踪如何在吻合任务期间实时、准确地识别和跟踪组织目标，与目前在体模和体内研究中的跟踪方法形成对比。目的3：比较有监督的自主机器人控制和人工吻合术。在临床前研究中，我们将比较由有监督的自主机器人控制的自动缝合规划算法与当前标准的主从机器人和手动腹腔镜技术在体内吻合方面的差异。这项研究有可能显著改善吻合口的功能和结果，而不依赖于外科医生的经验。除吻合术外，在所有因工作空间狭小而需要精确度和可操作性的软组织管理系统和RAS任务中，包括儿科和复杂的心脏手术，采用这种方法可能是有益的。

 DESCRIPTION (provided by applicant): Anastomosis is a necessary and critical part of all reconstructive surgery involving any luminal structure from cardiovascular to gastrointestinal (GI) surgery. Well over a million anastomoses are performed in the USA each year for visceral indications alone (gastrointestinal, urologic and gynecologic surgery). However, up to 30% of GI anastomoses are complicated by leakage, strictures, and stenosis, in part attributable to technical and technologic issues. An anastomotic complication significantly increases patient mortality from three times up to ten times, and diminishes the function and quality of life for affected patients. Although the minimally invasive surgical approach has transformed surgery with significantly reduced collateral tissue damage associated with access to operative sites, recent advances in surgical tools and vision technology have not addressed the critical factors influencing anastomotic outcome. This is evidenced by the lack of improvements in complication rates. To the contrary, the current minimally invasive surgery (MIS) or robot assisted surgery (RAS) pose additional new challenges for anastomosis stemming from visual and spatial limitations. The long-term goal of this research is to reduce complications and improve functional outcomes of anastomosis by robotically executing best anastomosis techniques. The following specific aims will enable the development of this technology and demonstrate feasibility, as a path to clinical adoption: Aim 1: Identify optimal suture placements using multispectral imaging. We will compare suture placements and anastomotic outcome between those guided by our novel algorithm for suture location optimization incorporating subsurface anatomic and physiologic information and those performed by expert surgeons in pre-clinical studies. Aim 2: Accurately track mobile and deformable soft tissue targets in an unstructured surgical environment. We will demonstrate how our innovative fused 3D tracking based on plenoptic imaging and NIR marker technology allows real-time, accurate identification and tracking of tissue targets during the task of anastomosis in contrast to current tracking methods in phantom and in-vivo studies. Aim 3: Compare supervised autonomous robotic control to manual anastomosis. We will compare the algorithm of automated suture planning controlled by supervised autonomous robotics to current standard master-slave robotic and manual laparoscopic technology in performing in-vivo anastomosis in preclinical studies. This research has the potential to significantly improve the function and outcome of anastomosis, independent of surgeon experience. Beyond anastomosis, adoption of this approach could be beneficial in all soft tissue MIS and RAS tasks requiring precision and maneuverability due to small working space, including pediatric and complex cardiac surgery.