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Surgical phase recognition is a challenging and necessary task for thedevelopment of context-aware intelligent systems that can support medicalpersonnel for better patient care and effective operating room management. Inthis paper, we present a surgical phase recognition framework that employs aMulti-Stage Temporal Convolution Network using speech and X-Ray images for thefirst time. We evaluate our proposed approach using our dataset that comprises31 port-catheter placement operations and report 82.56 \% frame-wise accuracywith eight surgical phases. Additionally, we investigate the design choices inthe temporal model and solutions for the class-imbalance problem. Ourexperiments demonstrate that speech and X-Ray data can be effectively utilizedfor surgical phase recognition, providing a foundation for the development ofspeech assistants in operating rooms of the future.

PoCaPNet: A Novel Approach for Surgical Phase Recognition Using Speech and X-Ray Images