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About

Sangha Song is a researcher specializing in medical robotics, with a focus on enhancing the precision and safety of minimally invasive procedures. Her key research areas include percutaneous cancer treatments and cosmetic surgery robotics, where she develops intelligent systems to improve clinical outcomes. Song’s major contributions center on force pattern analysis and texture-based detection for robotic assistance. In her most cited work, "Event classification in percutaneous treatments based on needle insertion force pattern analysis" (2013, 10 citations), she pioneered methods to classify surgical events by analyzing needle insertion forces, addressing the critical challenge of target miss in cancer biopsies and treatments. This work lays the foundation for robotic systems that can adapt in real-time to tissue variations. Additionally, her study "Detection of dermis and fascia on skin layers for liposuction surgery robot using texture and geometric information" (2012, 5 citations) introduces quantitative approaches to reduce skin surface irregularities, a common side effect of liposuction. Song’s research, though with modest citation counts, demonstrates impactful innovation in surgical robotics, offering systematic solutions to complex procedural challenges. Her work is notable for bridging engineering and clinical practice, advancing robotic tools that promise greater accuracy and patient safety in both therapeutic and cosmetic surgeries.