用AI预测视网膜运动，让机器人精准完成眼底注射
Deep Learning-Enhanced Robotic Subretinal Injection with Real-Time Retinal Motion Compensation
- 用LSTM网络预测视网膜运动，比传统方法更准
- 实测针尖追踪误差低于16.4微米，定位极精准
- 适合眼科手术机器人研发与微创治疗研究者
视网膜下注射是治疗年龄相关性黄斑变性等眼病的关键技术，但呼吸、心跳等生理因素引起的视网膜运动严重影响针头精确定位，易损伤视网膜色素上皮（RPE）。本文提出一种全自动机器人视网膜下注射系统，结合术中光学相干断层扫描（iOCT）成像与深度学习运动预测，实现针头运动与视网膜位移的实时同步。采用长短期记忆（LSTM）神经网络预测内界膜（ILM）运动，性能优于基于快速傅里叶变换（FFT）的基线模型。同时构建实时配准框架，将针尖位置与机器人坐标系对齐，并设计动态比例速度控制策略，确保插入过程平滑自适应。在仿真和离体猪眼实验中均验证了精确运动同步与成功注射。预插入阶段平均追踪误差低于16.4 μm。结果表明，人工智能驱动的机器人辅助可显著提升眼底微创手术的安全性与准确性。
原文摘要 · Abstract (English)
Subretinal injection is a critical procedure for delivering therapeutic agents to treat retinal diseases such as age-related macular degeneration (AMD). However, retinal motion caused by physiological factors such as respiration and heartbeat significantly impacts precise needle positioning, increasing the risk of retinal pigment epithelium (RPE) damage. This paper presents a fully autonomous robotic subretinal injection system that integrates intraoperative optical coherence tomography (iOCT) imaging and deep learning-based motion prediction to synchronize needle motion with retinal displacement. A Long Short-Term Memory (LSTM) neural network is used to predict internal limiting membrane (ILM) motion, outperforming a Fast Fourier Transform (FFT)-based baseline model. Additionally, a real-time registration framework aligns the needle tip position with the robot's coordinate frame. Then, a dynamic proportional speed control strategy ensures smooth and adaptive needle insertion. Experimental validation in both simulation and ex vivo open-sky porcine eyes demonstrates precise motion synchronization and successful subretinal injections. The experiment achieves a mean tracking error below 16.4 μm in pre-insertion phases. These results show the potential of AI-driven robotic assistance to improve the safety and accuracy of retinal microsurgery.
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