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To manage the influx of information into surgical practice, new man-machine interaction methods are necessary to prevent information overflow. This work presents an approach to automatically segment surgeries into phases and select the most appropriate pieces of information for the current situation. This way, assistance systems can adopt themselves to the needs of the surgeon and not the other way around.

Maschinelles Lernen --- Assistenz --- Ontologie --- Ontology --- Surgery --- Augmented Reality --- Chirurgie --- Erweiterte RealitätMachine Learning --- Assistance