Data and code from: Deep learning-based autonomous retinal vein cannulation in ex vivo porcine eyes

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) control a 100-micrometer metal needle and a
medical spatula to execute precise tasks. Three convolutional neural
networks are trained to predict needle movement direction and identify
contact and puncture events. A surgical microscope with an intraoperative
Optical Coherence Tomography (iOCT) system captures the surgical field
through a microscope and cross-sectional images. The goal is 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 are assigned to the user, who also
supervises the robot during these steps. Our method is tested on 20 ex
vivo porcine eyes, achieving a success rate of 90 %. Additionally, we
simulate eye movements caused by breathing on six other ex vivo porcine
eyes. With the eyes moving in a sinusoidal pattern, we achieve 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.