Qianqian Li

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About

Qianqian Li is a researcher whose work sits at the critical intersection of biomedical signal processing and robotic spinal surgery. Her primary research focus is on enhancing intraoperative neural safety by developing automated methods to detect nerve root proximity using electromyography (EMG) signals. Her most notable contribution, published in 2022, presents a novel approach that leverages the internal connection between EMG signals and surgical instrument positioning to provide real-time neural threat detection during robot-assisted procedures. This work, which has garnered 3 citations, addresses a fundamental challenge in spinal surgery: the risk of iatrogenic nerve injury. By proposing an automated, signal-based method to replace or augment traditional manual monitoring, Li’s research directly contributes to making robotic spinal surgery safer and more reliable. Her work is particularly relevant for the growing field of surgical robotics, where precise, real-time feedback is essential. While still early in her career, Li has established a focused research trajectory that promises to improve surgical outcomes and patient safety. Her contributions are of significant interest to biomedical engineers, spine surgeons, and researchers developing next-generation surgical robotic systems.