SenSE: Smart Grasper for Intelligent Surgical Manipulation

该项目将开发一种新颖的创新智能手术仪器，作为一种多模式传感系统，以促进外科保健。现代手术模式，包括微创腹腔镜手术和机器人辅助手术，剥夺了外科医生重要的触觉信息，本项目旨在部分恢复和扩展到新的维度。该项目建立了一个微型多模式系统，其中包括光学、电、热、机械和超声波传感器，这些传感器被固定在基本的手术设备上，如抓取器或手术刀(手持或由先进的手术机器人使用)。这将使该设备成为一种诊断仪器，能够测量血液氧合(SpO2)、身体损伤，以及潜在的其他组织状态，如癌症。需要机器智能的进步来将这些原始的、多维的数据处理成对外科医生临床有用的信息。这一新的系统将促进在复杂的手术情况下更安全和更有效地操作组织，保护容易无意中损坏的脆弱组织或难以肉眼识别的组织。该项目将支持研究生和本科生，包括来自代表人数不足的群体的学生学习STEM，并支持私人投资机构和学生参与旨在促进和扩大参与STEM教育方案的外联活动。该项目由一个多学科团队提出，旨在通过将先进的机器学习与旨在改善手术结果的新型多模式传感系统相结合来促进健康发现。该项目将首次在体内同时测量多个功能器官中多个部位的脉搏血氧饱和度(血氧饱和度，SO2)、温度、电阻抗(1-100 kHz)、压力和声学特性。它将开发、测试和展示一种新型的、高度集成的、适合内窥镜使用的多模式传感手术抓取器。该仪器将产生一种针对组织外科工作的新型传感数据的连续流。该项目将生成第一个这样的体外和体内数据数据库，以及来自手术视频、成像研究、专家评估和手术后组织病理分析的基本事实。新的数据集将通过一个已实现的开放存储库在网上公开共享。新的分类和分类机器学习系统将测量的组织属性与器官解剖学和生理学相关，特别是在组织之间的界面上，帮助手术计划和执行，并可能支持新的临床护理模式。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project will develop a novel innovative smart surgical instrument as a multimodal sensing system to advance surgical health care. Modern surgical paradigms, including minimally invasive laparoscopic surgery and robotic assisted surgery, have deprived surgeons of important tactile information that this project aims to partially restore and expand into new dimensions. This project builds a tiny multimodal system that includes optical, electrical, thermal, mechanical and ultrasound sensors that is affixed to a basic surgical device such as a grasper or scalpel (either hand-held or used by an advanced surgical robot). This will turn the device into a diagnostic instrument able to measure blood oxygenation (SpO2), physical damage, and potentially other tissue states like cancer. Advances in machine intelligence are required to process this raw, multidimensional, data into information that is clinically useful to the surgeon. This novel system will promote safer and more effective manipulation of tissues in complex surgical situations protecting delicate tissues easily damaged unintentionally or tissues that are hard to visually identify. The project will support graduate and undergraduate students, including from underrepresented groups, in their STEM studies, and support participation by the PIs and students in outreach events designed to promote and broaden participation in STEM educational programs. This project, proposed by a multidisciplinary team, aims to advance health discovery through combining advanced machine learning with a novel multimodal sensing system aimed at improved surgical outcomes. This project will obtain the first ever simultaneous in-vivo measurements of pulse oximetry (blood oxygen saturation, SO2), temperature, electrical impedance (1-100kHz), pressure, and acoustic properties at multiple sites in multiple functioning organs. It will develop, test, and demonstrate a novel, highly integrated, multimodal sensing surgical grasper suitable for endoscopic use. The instrument will generate a continuous stream of a new type of sensed data specific to surgical work with tissues. This project will generate the first such database of in-vitro and in-vivo data along with ground truth from surgical videos, imaging studies, expert evaluations, and post-operative pathological analysis of tissues. The new data set will be shared openly online via an achieved open repository. The new classification and clustering machine learning systems will correlate measured tissue properties with organ anatomy and physiology, especially at interfaces between tissues, aiding in surgical planning and execution, and potentially supporting new modes of clinical care.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.