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About

Anna Kisilenko is a researcher at the forefront of computer-assisted surgery, with a particular focus on surgical workflow analysis, semantic segmentation, and intelligent systems for laparoscopic procedures. Her work sits at the intersection of machine learning, computer vision, and surgical robotics, addressing one of healthcare's most pressing challenges: making operating rooms safer and more efficient through context-aware artificial intelligence. Kisilenko's most celebrated contribution is her involvement in the HeiChole benchmark study, a landmark comparative validation of machine learning algorithms for surgical workflow and skill analysis, which has accumulated 96 citations since 2023 and represents a significant community resource for researchers in the field. Her earlier work on deep learning for semantic segmentation of organs and tissues in laparoscopic surgery, garnering 40 citations, laid important groundwork for scene understanding in minimally invasive procedures. More recently, she has turned her attention to collaborative surgical robots, developing detailed surgical activity models for laparoscopic cholecystectomy to enable genuine human-robot cooperation in the operating theatre. Rounding out her portfolio, she has even explored flexible tactile sensing for smart medical phantoms, demonstrating a breadth of technical curiosity that makes her a distinctive voice in surgical technology research.