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About

Enes Hosgor is a leading researcher at the intersection of computer vision and surgical data science, whose work is driving the next generation of cognitive surgical assistance. His primary research focuses on developing and validating machine learning algorithms for surgical workflow analysis, instrument segmentation, and skill assessment. Hosgor’s major contributions include co-organizing the ROBUST-MIS 2019 challenge, which established a benchmark for multi-instance instrument segmentation in endoscopy—a critical step toward intraoperative tracking for robotic-assisted interventions. His landmark study, “Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark” (2023), has garnered 96 citations, providing a standardized framework for evaluating algorithms that could enable context-sensitive warnings and semi-autonomous robotic assistance. Additionally, his work on the HeiChole benchmark (2021) and the ROBUST-MIS challenge (2020, 89 citations) has collectively shaped how researchers assess surgical tool detection and workflow recognition. With over 200 combined citations, Hosgor’s contributions are foundational for improving surgical safety and training through intelligent, data-driven systems.