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A Deep Learning-Driven Autonomous System for Retinal Vein Cannulation: Validation Using a Chicken Embryo Model
Yi Wang1, Peiyao Zhang2, Mojtaba Esfandiari2
1Yi Wang is with the Department of Robotics and also the Laboratory for Computational Sensing and Robotics at the Johns Hopkins University, Baltimore, MD, 21218, USA.
Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems
|December 16, 2025
Summary
This study introduces an automated robotic system for retinal vein cannulation (RVC), improving precision and reducing procedure time. The system uses deep learning and OCT imaging for accurate needle navigation and puncture recognition in RVO treatment.
Area of Science:
- Ophthalmology
- Robotics
- Medical Imaging
Background:
- Retinal vein occlusion (RVO) causes vision loss, and its treatment, retinal vein cannulation (RVC), is technically challenging due to delicate retinal veins.
- Microsurgical RVC requires high precision and tremor-free manipulation, often necessitating advanced tools for improved outcomes.
Purpose of the Study:
- To develop and evaluate an automated robotic system for precise retinal vein cannulation.
- To enhance the accuracy and stability of RVC procedures through robotic assistance and advanced imaging.
Main Methods:
- An automated robotic system was developed, incorporating a top-down microscope and B-scan optical coherence tomography (OCT) for depth sensing.
- Deep learning models were utilized for real-time needle navigation, contact detection, and puncture recognition.
- A chicken embryo model served as a surrogate for human retinal veins to test the system's efficacy.
Main Results:
- The automated system achieved 85% accuracy in detecting needle position and puncture events.
- Significant reductions in navigation and puncture times were observed compared to manual RVC methods.
- The system demonstrated reliable performance in autonomous microsurgical tasks.
Conclusions:
- The integration of advanced imaging and deep learning in robotic systems offers a promising approach for automating microsurgical tasks.
- This technology has the potential to make retinal vein cannulation safer, more precise, and reproducible.
- The developed system paves the way for improved treatments for retinal vein occlusion.
