Summary
Robotic eye surgery is very precise but makes it hard for surgeons to feel what they are touching. To fix this, the authors created a system that sends touch sensations back to the surgeon using a special model called a Scene Graph, which understands the surgical area from simulated imaging. This system helps surgeons better guide their tools by giving them important feedback at the right moments, making surgeries safer and more accurate. In tests, using this feedback reduced errors and made the system easier to use without taking more time. It also helped surgeons follow a safer approach by adjusting tool movements before getting close to sensitive areas.
Robotic ophthalmic surgeryHaptic feedbackScene GraphIntraoperative Optical Coherence Tomography (iOCT)Subretinal injectionTactile feedbackSurgical precisionSystem Usability Scale (SUS)Safety-enhancing feedbackRobotic input device
Authors
Danial Arbabi, Korab Hoxha, Angelo Henriques, Mirza Imamovic, M. Ali Nasseri
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.