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

 描述（由申请人提供）：吻合术是从心血管到胃肠道（GI）手术的所有涉及任何管腔结构的重建手术的必要和关键部分。在美国，每年仅内脏适应症（胃肠道、泌尿外科和妇科手术）就进行超过100万例手术。然而，高达30%的胃肠道疾病并发渗漏、狭窄和狭窄，部分原因是技术和工艺问题。吻合口并发症使患者死亡率显著增加，从3倍增加到10倍，并降低受影响患者的功能和生活质量。尽管微创手术方法已经改变了手术，显著减少了与手术部位相关的附带组织损伤，但手术工具和视觉技术的最新进展尚未解决影响吻合结局的关键因素。并发症发生率没有改善证明了这一点。相反，目前的微创手术（MIS）或机器人辅助手术（RAS）由于视觉和空间限制而对吻合术提出了额外的新挑战。 本研究的长期目标是通过机器人执行最佳吻合技术来减少并发症并改善吻合的功能结局。以下具体目标将使该技术的开发成为可能，并证明其可行性，作为临床采用的途径：目标1：使用多光谱成像确定最佳缝线位置。我们将比较由我们的结合皮下解剖和生理信息的缝合位置优化新算法指导的缝合位置和吻合结果，以及由临床前研究中的专家外科医生执行的缝合位置和吻合结果。目标2：在非结构化手术环境中准确跟踪移动的和可变形的软组织目标。我们将展示我们基于全光成像和NIR标记技术的创新融合3D跟踪如何在吻合任务期间实现组织目标的实时、准确识别和跟踪，与目前在体模和体内研究中的跟踪方法形成对比。目的3：比较监督自主机器人控制与手动吻合。我们将在临床前研究中比较由监督自主机器人控制的自动缝合计划算法与当前标准主从机器人和手动腹腔镜技术在体内吻合中的应用。 这项研究有可能显著改善吻合术的功能和结局，与外科医生的经验无关。除吻合术外，由于工作空间小，采用这种方法可能有利于所有需要精确度和可操作性的软组织MIS和RAS任务，包括儿科和复杂的心脏手术。

 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.