Yinyu Nie

Bournemouth University, Technical University of Munich

Papers

Total Citations

H-Index

About

Yinyu Nie is a leading researcher in surgical data science, with a focus on computer vision and deep learning for intraoperative analysis. Her work centers on advancing surgical gesture recognition and skill assessment, aiming to provide real-time, context-aware assistance during procedures without relying on additional sensors. Her most-cited paper, "Symmetric Dilated Convolution for Surgical Gesture Recognition" (2020), with 31 citations, introduced a novel architecture that captures long-range temporal dependencies more effectively than prior methods. Building on this, her 2021 paper "SD-Net: joint surgical gesture recognition and skill assessment" (15 citations) proposed an integrated framework that simultaneously recognizes gestures and evaluates surgeon proficiency, addressing a critical gap in automated surgical training. These contributions have significant implications for improving surgical workflow efficiency, reducing errors, and enabling objective skill evaluation. Nie’s work is notable for its practical focus on leveraging existing video data, making it accessible for real-world clinical deployment. Her research continues to shape the future of intelligent surgical systems.