用深度学习+OCT实现视网膜静脉自动穿刺，准确率达85%
A Deep Learning-Driven Autonomous System for Retinal Vein Cannulation: Validation Using a Chicken Embryo Model
- 结合显微镜与B型OCT，用深度学习实时导航和识别穿刺
- 自动穿刺准确率85%，导航与穿刺时间显著低于人工
- 适合微创眼科手术自动化研究者参考
视网膜静脉穿刺（RVC）是治疗视网膜静脉阻塞（RVO）这一主要致盲原因的微创微外科手术。然而，视网膜静脉细小脆弱，需高精度、无震颤的针头操作，带来巨大技术挑战。本研究提出一种自动机器人系统，采用俯视显微镜与B型光学相干断层扫描（OCT）进行精确深度感知。基于深度学习的模型实现针头实时导航、接触检测与穿刺识别，使用鸡胚胎模型作为人眼视网膜静脉的替代。系统对针头位置和穿刺事件的自动检测准确率达85%。实验表明，与手动方法相比，导航与穿刺时间显著减少。结果证明，融合先进成像与深度学习可实现微外科任务自动化，为更安全、可靠、高精度和可重复的RVC提供可行路径。
原文摘要 · Abstract (English)
Retinal vein cannulation (RVC) is a minimally invasive microsurgical procedure for treating retinal vein occlusion (RVO), a leading cause of vision impairment. However, the small size and fragility of retinal veins, coupled with the need for high-precision, tremor-free needle manipulation, create significant technical challenges. These limitations highlight the need for robotic assistance to improve accuracy and stability. This study presents an automated robotic system with a top-down microscope and B-scan optical coherence tomography (OCT) imaging for precise depth sensing. Deep learning-based models enable real-time needle navigation, contact detection, and vein puncture recognition, using a chicken embryo model as a surrogate for human retinal veins. The system autonomously detects needle position and puncture events with 85% accuracy. The experiments demonstrate notable reductions in navigation and puncture times compared to manual methods. Our results demonstrate the potential of integrating advanced imaging and deep learning to automate microsurgical tasks, providing a pathway for safer and more reliable RVC procedures with enhanced precision and reproducibility.
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