Augmenting the Reach: Visualizing Robotic Working Volume at the Tool Tip for Intuitive Retinal Access in Eye Surgery
Junjie Yang, Satoshi Inagaki, Zhihao Zhao, Daniel Zapp, Kai Huang, M. Ali Nasseri
AI summary
Problem
Compact ophthalmic robots sacrifice working volume for precision, leaving surgeons unable to intuitively verify if the tool tip can reach specific retinal targets. Existing theoretical models often fail to match actual intraocular accessibility due to calibration and optical errors.
Approach
The method calculates the robot’s conic working volume in 3D space using trocar position and shadow-based touch approximation, then projects its boundary directly onto the live 2D microscope feed as a visual overlay.
Key results
- Validated on a commercial eye phantom with ≤1.0° angular error for 83.3% of tested points
- Correctly predicted intraocular accessibility for 83.3% of 48 targets across four retinal subareas
- Identified 16.7% of points as unexpectedly inaccessible to guide robotic reconfiguration
- Established a practical task flow for real-time 2D-3D retinal coverage visualization
Why it matters
Enables ophthalmic surgeons to confidently plan and adjust robotic configurations for complete target coverage, improving safety and efficacy in minimally invasive eye surgery.
Abstract
Retinal Surgery Robotics is a rapidly emerging field that offers enhanced precision by overcoming human tremors. A key trend of these robotic designs is toward more compact and lightweight structures for improved positioning accuracy and precise force delivery. However, this compactness sacrifices the robot’s working volume, making it difficult for ophthalmic surgeons to intuitively assess if retinal targets are accessible by the surgical tool tip. This paper proposes a methodology for visualizing the actual accessible area in the microscopic view to provide surgeons with an intuitive visual guide of the tool’s reach, reducing uncertainty and streamlining extraocular robotic maneuvers. We validated this method on a commercial phantom with a surgical robot system, achieving ⩽1.0 deg error for 83.3% of tested points across four retinal subareas and demonstrating its clinical potential.