Ophthalmic microsurgery is technically difficult because the scale of required surgical tool manipulations challenge the limits of the surgeon's visual acuity, sensory perception, and physical dexterity. Intraoperative optical coherence tomography (OCT) imaging with micrometer-scale resolution is increasingly being used to monitor and provide enhanced real-time visualization of ophthalmic surgical maneuvers, but surgeons still face physical limitations when manipulating instruments inside the eye. Autonomously controlled robots are one avenue for overcoming these physical limitations. In this article, we demonstrate the feasibility of using learning from demonstration and reinforcement learning with an industrial robot to perform OCT-guided corneal needle insertions in an ex vivo model of deep anterior lamellar keratoplasty (DALK) surgery. Our reinforcement learning agent trained on ex vivo human corneas, then outperformed surgical fellows in reaching a target needle insertion depth in mock corneal surgery trials. This article shows the combination of learning from demonstration and reinforcement learning is a viable option for performing OCT-guided robotic ophthalmic surgery.

中文翻译：

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光学相干断层扫描引导的机器人眼科显微手术通过演示中的强化学习

眼科显微手术在技术上是困难的，因为所需的手术工具操作的规模挑战了外科医生的视力、感官知觉和身体灵活性的极限。具有微米级分辨率的术中光学相干断层扫描 (OCT) 成像正越来越多地用于监测和提供增强的眼科手术操作的实时可视化，但外科医生在操作眼内仪器时仍然面临物理限制。自主控制的机器人是克服这些物理限制的一种途径。在本文中，我们展示了使用演示学习和强化学习与工业机器人在深前板层角膜移植 (DALK) 手术的离体模型中执行 OCT 引导的角膜针插入的可行性。我们的强化学习代理在离体人类角膜上进行了训练，然后在模拟角膜手术试验中达到目标针插入深度方面的表现优于外科医生。本文展示了从演示中学习和强化学习相结合是执行 OCT 引导的机器人眼科手术的可行选择。