Vitreoretinal Surgery via Robotic Microsurgical System with Image Guidance, Force Feedback, Virtual Fixture, and Augmented Reality

通过具有图像引导、力反馈、虚拟夹具和增强现实功能的机器人显微手术系统进行玻璃体视网膜手术

• 批准号: 10582637
• 负责人: JACOB ROSEN
• 金额: $ 40.18万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10582637
• 关键词: 3-Dimensional; Abate; Affect; Age related macular degeneration; Aging; Anatomy; Augmented Reality; Biological; Blindness; Cannulations; Cataract Extraction; Clinical; Complete Blindness; Complex; Data; Dedications; Detection; Development; Diabetes Mellitus; Diabetic Retinopathy; Disease; Dissection; Engineering; Ensure; Environment; Epidemic; Epiretinal Membrane; Evaluation; Excision; Eye; Eye Surgeon; Failure; Family suidae; Feedback; Foundations; Functional disorder; Funding; Goals; Grant; Hand; Health; Human; Iatrogenesis; Image; Individual; Intervention; Location; Mechanics; Membrane; Microscope; Microsurgery; Modeling; Motion; Motivation; Movement; Operative Surgical Procedures; Optical Coherence Tomography; Outcome; Pathology; Patients; Perception; Performance; Physiological; Population; Procedures; Process; Protocols documentation; Psyche structure; Raven; Research; Resolution; Retina; Retinal Diseases; Retinal Perforations; Robot; Robotics; Safety; Scanning; Science; Series; Speed; Structure; Structure of central vein of the retina; Surgeon; Surgical Instruments; Surgical complication; System; Tactile; Time; Tissues; Translating; Uncertainty; United States National Institutes of Health; Vision; Visual; Visual impairment; Visualization; Vitrectomy; Work; auditory feedback; cognitive load; cost; design; experience; fabrication; force feedback; haptic feedback; human tissue; image guided; improved; innovation; interest; lens; meter; microscopic imaging; minimally invasive; multimodality; new technology; open source; prototype; robot control; robot interface; robotic system; sensory feedback; subretinal injection; success; surgery outcome; teleoperation; tool; virtual; virtual reality simulator; visual feedback

PROJECT SUMMARY/ABSTRACT The aims of the present proposal are to extend our work with the Intraocular Robotic Interventional and Surgical System (IRISS) and augment it for teleoperated vitreoretinal surgery. Although novel technologies such as intraoperative optical coherence tomography (i-OCT) have been developed, vitreoretinal surgeons still lack critical information during surgery (e.g., the distance between pre-retinal membrane and retina) due to inadequate display and feedback. In addition, physiological capabilities are a limiting factor because the retina is one of the smallest and most delicate tissues of the human body. The rate of surgical failure in complex retinal cases remains high (10–15%) due to the limits of current surgical capabilities, thereby condemning these patients to blindness [1-3]. Our group has developed the IRISS [4-10] through a combination of internal funding and a recent R21 grant (NIH/R21EY024065). This support enabled our group to develop the IRISS platform to perform fully automated cataract surgery on ex-vivo pig eyes. We have also demonstrated the ability of the IRISS to perform safe- motion guidance for lens removal based on per-operative, real-time anatomical detection, and teleoperated capabilities for vitreoretinal maneuvers, including retinal vein cannulation and core vitrectomy [4]. Furthermore, Raven II, an open-source surgical robotics system [11-22], was co-developed by Rosen over the past 16 years for general minimally invasive surgery. In the present study, the surgical cockpit of the Raven II system will serve as the foundation of the user interface for the improved robotic surgical system. The accumulated experience of our group through this previous work will guide the proposed research effort from the stringent clinical requirements to the design, development, and evaluation of the proposed system. The present study is composed of three independent, parallel tracks. First, the mechanical design and assembly of the robotic surgical system will be improved to achieve tool-tip positional precision of 5 µm, approximately ten times more precise than a human surgeon [23]. Second, we will enhance the surgeon's abilities in sensing and interpreting anatomical details during retinal manipulation by applying high-resolution (10 µm), real-time intraoperative i-OCT scans to detect anatomical features critical to specific vitreoretinal procedures. Third, surgical features of interest will be presented to the surgeon via a human–robot surgical cockpit that provides innovative 3D, augmented-reality visualization and auditory and haptic feedback. Each aim will be assessed by a series of evaluation protocols to ensure their success. The safety and efficacy of the system will also be compared with and without the proposed improvements (robotic control, enhanced sensing, and augmented feedback) on a virtual reality simulator in addition to phantom and biological eye models chosen to best assess surgical outcome. It is important to note that while the ultimate goal is the integration of all three aims, their development remains independent and success or failure in one does not affect the outcome of another. We hypothesize that a surgeon–robot surgical system that incorporates enhanced sensing and feedback to enrich the surgeon's perception and interpretation of anatomical details will improve surgical safety and reduce the rate of surgical complications to improve health outcomes and abate the costs associated with surgical complications.

项目总结/文摘

项目成果

Automated Retinal Vein Cannulation on Silicone Phantoms Using Optical-Coherence-Tomography-Guided Robotic Manipulations.

• DOI: 10.1109/tmech.2020.3045875
• 发表时间: 2021-10
• 期刊: IEEE-ASME TRANSACTIONS ON MECHATRONICS
• 影响因子: 6.4
• 作者: Gerber, Matthew J.;Hubschman, Jean-Pierre;Tsao, Tsu-Chin
• 通讯作者: Tsao, Tsu-Chin

A Novel Tissue Identification Framework in Cataract Surgery Using an Integrated Bioimpedance-Based Probe and Machine Learning Algorithms.

使用基于生物阻抗的集成探针和机器学习算法的白内障手术中的新型组织识别框架。

• DOI: 10.1109/tbme.2021.3109246
• 发表时间: 2022
• 期刊: IEEE transactions on bio-medical engineering
• 作者: AghajaniPedram,Sahba;Ferguson,Peter;Gerber,Matthew;Shin,Changyeob;Hubschman,J-P;Rosen,Jacob
• 通讯作者: Rosen,Jacob