Scene Graph-Driven Haptic Feedback for Safety Enhancement in Robotic Ophthalmic Surgery via Physically Simulated iOCT

2026-09-07

AI Summary

本文通过构建基于场景图的触觉反馈系统，利用物理模拟iOCT解决机器人眼科手术中因缺乏触觉反馈导致的精度问题。

Abstract

Robotic ophthalmic surgery offers high precision but introduces a"sensory gap"by decoupling the surgeon from their instrument, resulting in a loss of tactile feedback. This paper presents a novel haptic feedback system for subretinal injection tasks leveraging Scene Graphs (SG). The system bridges the sensory gap by analyzing a physically simulated intraoperative Optical Coherence Tomography (iOCT) feed to construct a real-time surgical SG. The SG serves as a semantic abstraction layer for the surgical scene, which is then utilized by a deterministic, rule-based engine to generate state-dependent haptic feedback on a robotic input device. The system was evaluated in a user study (N=16) using an anthropomorphic head phantom and a custom-built surgical robot. Results demonstrate that the SG-driven haptic feedback improved surgical precision, reducing needle alignment error by 14% (p = 0.044) and improving System Usability Scale (SUS) scores by 8% (p = 0.015), while maintaining comparable task completion times. A needle trajectory analysis revealed the emergence of a safer"Align-then-Approach"strategy, in which our haptic negative reinforcement prompted users to fine-tune the tool's trajectory before approaching the retinal target. This work suggests that SGs can effectively serve as the direct computational foundation for real-time, safety-enhancing context-aware haptic feedback in robotic microsurgery.

Problem

• Robotic Ophthalmic Surgery
• Haptic Feedback
• Scene Graphs
• Sensory Gap

Innovation

• Scene Graph
• Haptic Feedback
• Physically Simulated iOCT
• Surgical Precision
• Safety Enhancement