A Deep Learning-Driven Autonomous System for Retinal Vein Cannulation: Validation Using a Chicken Embryo Model

TL;DR

This paper introduces an automated robotic system utilizing deep learning and OCT imaging for precise retinal vein cannulation, validated on a chicken embryo model, aiming to improve safety and accuracy in microsurgery.

Contribution

It presents a novel integration of deep learning, OCT imaging, and robotic assistance for autonomous retinal vein cannulation, validated on a biological model.

Findings

Needle detection accuracy of 85%

Reduced navigation and puncture times

Potential for safer, more precise microsurgery

Abstract

Retinal vein cannulation (RVC) is a minimally invasive microsurgical procedure for treating retinal vein occlusion (RVO), a leading cause of vision impairment. However, the small size and fragility of retinal veins, coupled with the need for high-precision, tremor-free needle manipulation, create significant technical challenges. These limitations highlight the need for robotic assistance to improve accuracy and stability. This study presents an automated robotic system with a top-down microscope and B-scan optical coherence tomography (OCT) imaging for precise depth sensing. Deep learning-based models enable real-time needle navigation, contact detection, and vein puncture recognition, using a chicken embryo model as a surrogate for human retinal veins. The system autonomously detects needle position and puncture events with 85% accuracy. The experiments demonstrate notable reductions in navigation and puncture times compared to manual methods. Our results demonstrate the potential of integrating advanced imaging and deep learning to automate microsurgical tasks, providing a pathway for safer and more reliable RVC procedures with enhanced precision and reproducibility.