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

Yang SH, Demir K, Weise T, Schmid A, May M, Maier A (2023)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2023

Pages Range: 5

Event location: Dublin

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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How to cite

APA:

Yang, S.H., Demir, K., Weise, T., Schmid, A., May, M., & Maier, A. (2023). PoCaPNet: A Novel Approach for Surgical Phase Recognition Using Speech and X-Ray Images. In Proceedings of the International Conference Interspeech 2023 (pp. 5). Dublin, IE.

MLA:

Yang, Seung Hee, et al. "PoCaPNet: A Novel Approach for Surgical Phase Recognition Using Speech and X-Ray Images." Proceedings of the International Conference Interspeech 2023, Dublin 2023. 5.

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