Yao Lyu

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About

Yao Lyu is a researcher advancing the field of surgical data science, with a focus on automated analysis of surgical video. Their key research areas include surgical gesture recognition, skill assessment, and temporal modeling using deep learning. Lyu’s major contributions center on developing novel convolutional architectures that capture long-range temporal dependencies without requiring additional sensors. Their seminal work, “Symmetric Dilated Convolution for Surgical Gesture Recognition” (2020, 31 citations), introduced an efficient method to model temporal patterns in surgical procedures. Building on this, Lyu proposed SD-Net (2021, 15 citations), a unified framework that jointly performs surgical gesture recognition and skill assessment, demonstrating how these tasks can benefit from shared representations. This work addresses a critical gap in context-aware intraoperative assistance and clinical resource scheduling. Lyu’s research has been recognized for its practical impact, offering scalable solutions that rely solely on video data. Their contributions are particularly valuable for students and researchers interested in computer vision applications in medicine, temporal sequence modeling, and the development of intelligent surgical systems that enhance both training and real-time decision-making.