A Feasible Workflow for Retinal Vein Cannulation in Ex Vivo Porcine Eyes with Robotic Assistance

TL;DR

This work addresses retinal vein cannulation (RVC) challenges posed by hand tremor and limited depth perception by introducing a robot-assisted workflow using two Steady Hand Eye Robots, a $100\,mu m$ beveled needle, keyboard control, and intraoperative OCT feedback. In 12 ex vivo porcine eyes, the approach achieved $10/12$ cannulations (83.33%), verified via iOCT imaging, demonstrating feasibility of tremor-free, precisely controlled puncture and infusion with maintained RCM. The study outlines a six-step procedure and provides insight into instrument control, imaging verification, and the practical limits of cadaveric tissue, highlighting pathways toward automation and in vivo validation. Future work aims to reduce needle size for complete cannulation, implement machine learning-driven automation, and address eye motion to move toward clinical trials.

Abstract

A potential Retinal Vein Occlusion (RVO) treatment involves Retinal Vein Cannulation (RVC), which requires the surgeon to insert a microneedle into the affected retinal vein and administer a clot-dissolving drug. This procedure presents significant challenges due to human physiological limitations, such as hand tremors, prolonged tool-holding periods, and constraints in depth perception using a microscope. This study proposes a robot-assisted workflow for RVC to overcome these limitations. The test robot is operated through a keyboard. An intraoperative Optical Coherence Tomography (iOCT) system is used to verify successful venous puncture before infusion. The workflow is validated using 12 ex vivo porcine eyes. These early results demonstrate a successful rate of 10 out of 12 cannulations (83.33%), affirming the feasibility of the proposed workflow.

Figures (7)

• Figure 1: Experimental setup: two SHERs control the needle and the medical spatula; Leica iOCT provides a top-down microscope view and B-scans; the vitrectomy machine provides light source and infusion pressure; the force sensor measures the handle force.
• Figure 2: Two views of the needle tip before and after needle polish.
• Figure 3: Workflow of proposed robot-assisted RVC method.
• Figure 4: Time distributions for each step.
• Figure 5: B-scans of targeted blood vessels after infusion.