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

2025-07-29

AI Summary

Minimally invasive treatment of retinal vein occlusion (RVO) is hindered by the fragility and small caliber of retinal veins, stringent requirements for micrometer-level precision, and insufficient stability in manual cannulation. To address this, we present the first fully autonomous robotic system for retinal vein cannulation (RVC), integrating B-scan optical coherence tomography (OCT) with deep learning to enable real-time 3D needle-tip localization, vessel contact detection, and puncture event recognition. The system comprises a top-mounted surgical microscope, an OCT imaging module, a high-precision robotic manipulator, and a closed-loop control algorithm. Validated in a chicken embryo model, it achieves fully autonomous intravenous puncture. Experimental results demonstrate 85% accuracy in needle positioning and puncture identification, significantly reduced navigation and puncture time, and superior procedural stability and repeatability compared to manual intervention. This work establishes a clinically translatable technical paradigm for precise, minimally invasive RVO therapy.

Technology Category

• Computer Vision: Vision for Robotics & Autonomous Driving
• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• Automating retinal vein cannulation for precision microsurgery
• Overcoming fragility and tremor challenges in RVC procedures
• Integrating deep learning and OCT for real-time navigation

Innovation

• Deep learning for real-time needle navigation
• B-scan OCT imaging for depth sensing
• Automated robotic system for precision