Automated retinal photocoagulation using instrument-integrated OCT and laser pattern mapping
Marius Briel, Ludwig Haide, Dongyue Wu, Justus Hornstein, Philipp Matten, Nicola Piccinelli, Gernot Kronreif, Eleonora Tagliabue, Franziska Mathis-Ullrich
AI summary
Problem
Manual retinal endolaser photocoagulation is highly repetitive and challenging to control precisely due to difficulties in estimating instrument-to-retina distance and mapping 2D laser patterns onto curved 3D retinal surfaces.
Approach
A robotic platform integrates instrument-mounted OCT for real-time depth sensing and employs spherical or ellipsoidal eye models to project 2D laser patterns onto 3D retinal geometry, dynamically corrected via a closed-loop feedback system.
Key results
- Clinical-level lateral (44 µm) and axial (29 µm) accuracy in ex vivo porcine eyes
- Optimized 3D mapping reduces pattern distortion by up to 70% compared to direct mapping
- Ellipsoidal models significantly outperform spherical models for larger treatment patterns
- Multi-mode control loop maintains consistent instrument-to-retina distances during high-speed operation
Why it matters
Reduces surgeon cognitive load and improves treatment uniformity, advancing the clinical viability of automated robotic assistance in ophthalmic microsurgery.
Abstract
Retinal endolaser photocoagulation (REPC) is a repetitive intraocular surgical procedure that could greatly benefit from automation and distance-based control, improving both efficiency and safety. This work presents a robotic system designed for automated REPC, utilizing instrument-integrated optical coherence tomography (iiOCT) to facilitate real-time distance measurements. The system employs intraoperative spherical and ellipsoidal retinal models to convert 2D laser patterns into 3D arrangements, which are further refined through a control loop that incorporates online feedback. Ex vivo experiments in porcine eyes demonstrated clinical-level accuracy, with lateral and axial errors of 44 μm and 29 μm, respectively. Additionally, the proposed mapping technique produced patterns with greater equidistance than baseline methods. This system showcases the potential to automate repetitive surgical tasks while maintaining the surgeon’s control over critical decision-making processes in ophthalmic surgery.