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arXiv · 2305.15993

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

Kubilay Can Demir·Tobias Weise·Matthias May·Axel Schmid·Andreas Maier·Seung Hee Yang
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Abstract

Surgical phase recognition is a challenging and necessary task for the development of context-aware intelligent systems that can support medical personnel for better patient care and effective operating room management. In this paper, we present a surgical phase recognition framework that employs a Multi-Stage Temporal Convolution Network using speech and X-Ray images for the first time. We evaluate our proposed approach using our dataset that comprises 31 port-catheter placement operations and report 82.56 \% frame-wise accuracy with eight surgical phases. Additionally, we investigate the design choices in the temporal model and solutions for the class-imbalance problem. Our experiments demonstrate that speech and X-Ray data can be effectively utilized for surgical phase recognition, providing a foundation for the development of speech assistants in operating rooms of the future.

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Kubilay Can Demir, Tobias Weise, Matthias May, Axel Schmid, Andreas Maier, Seung Hee Yang. 2023-05-25. PoCaPNet: A Novel Approach for Surgical Phase Recognition Using Speech and X-Ray Images. https://doi.org/10.21437/interspeech.2023-753

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