Sebastian Koller

Klinikum rechts der Isar, Technical University of Munich

Papers

Total Citations

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About

Sebastian Koller is a leading researcher in surgical data science, with a focus on developing intelligent intraoperative assistance systems that enhance minimally invasive procedures. His work sits at the intersection of computer vision, sensor-driven workflow recognition, and human–machine interface design. In his highly cited 2017 paper, Koller introduced sensor-based approaches for automatic surgical workflow recognition that operate without explicit models—a foundational contribution to the emerging field of surgical data science. This work, which has accumulated 26 citations, demonstrates how real-time data processing can support surgeons during complex operations. Koller also investigates the barriers to clinical adoption of advanced surgical platforms, as shown in his 2014 study on natural orifice translumenal endoscopic surgery (NOTES). By surveying surgeons, gastroenterologists, and medical engineers, he identified critical gaps in interface design and usability—work that has informed the development of more intuitive mechatronic support systems. Through these contributions, Koller is helping to bridge the gap between engineering innovation and clinical practice, advancing the vision of data-driven, context-aware surgical assistance that improves patient outcomes and surgeon performance.