Insights into Workflow Phase Recognition in Interventional Neuroradiology

Abstract The angiography suite for endovascular interventions has seen rapid medical and technical innovation in the past decade. In this highly complex work environment, context-aware support systems could simplify the interaction with the X-ray imaging system and therefore might increase efficiency. To learn about the context of endovascular interventions and recognize workflow phases, we collected and annotated a dataset of mechanical thrombectomy interventions. A classification model was trained for different tasks on several compositions of the dataset to gain an understanding for the challenges that arise when expanding the research field of surgical workflow detection to neuroradiological X-ray data. Implications were found that, despite limited visibility, device positioning is highly relevant and learned implicitly during model training. This paves the way to further enhance workflow phase recognition on interventional X-ray data by the integration of temporal context.