Real-Time Context-aware Detection of Unsafe Events in Robot-Assisted Surgery
This addresses safety risks for patients in surgical robotics, representing a domain-specific incremental improvement.
The paper tackles the problem of ensuring patient safety in robot-assisted surgery by developing a real-time safety monitoring system that detects unsafe events, achieving an average reaction time of 1,693 ms and F1 score of 0.88 for faults, and 57 ms and 0.76 for human errors.
Cyber-physical systems for robotic surgery have enabled minimally invasive procedures with increased precision and shorter hospitalization. However, with increasing complexity and connectivity of software and major involvement of human operators in the supervision of surgical robots, there remain significant challenges in ensuring patient safety. This paper presents a safety monitoring system that, given the knowledge of the surgical task being performed by the surgeon, can detect safety-critical events in real-time. Our approach integrates a surgical gesture classifier that infers the operational context from the time-series kinematics data of the robot with a library of erroneous gesture classifiers that given a surgical gesture can detect unsafe events. Our experiments using data from two surgical platforms show that the proposed system can detect unsafe events caused by accidental or malicious faults within an average reaction time window of 1,693 milliseconds and F1 score of 0.88 and human errors within an average reaction time window of 57 milliseconds and F1 score of 0.76.