Deep learning–based autonomous retinal vein cannulation in ex vivo porcine eyes

Abstract

Retinal vein cannulation (RVC) is an emerging method for treating retinal vein occlusion (RVO). The success of this procedure depends on surgeon expertise and, recently, robotic assistance. This paper proposes an autonomous RVC workflow leveraging deep learning and computer vision. Two Steady-Hand Eye Robots (SHERs) controlled a 100-micrometer metal needle and a medical spatula to execute precise tasks. Three convolutional neural networks were trained to predict needle movement direction and identify contact and puncture events. A surgical microscope with an intraoperative optical coherence tomography (iOCT) system captured the surgical field through a microscope and cross-sectional images (B-scans). The goal was to enable the robot to autonomously carry out the critical steps of the RVC procedure, especially those that are challenging and require expert knowledge. The less technically demanding tasks were assigned to the user, who also supervised the robot during these steps. Our method was tested on 20 ex vivo porcine eyes, achieving a success rate of 90%. In addition, we simulated eye movements caused by breathing on six other ex vivo porcine eyes. With the eyes moving in a sinusoidal pattern, we achieved a success rate of 83%, demonstrating the robustness and stability of the proposed workflow. Our results demonstrate that the autonomous RVC workflow, incorporating deep learning and robotic assistance, achieves high success rates in both static and dynamic conditions, indicating its potential to enhance the precision and reliability of RVO treatment.