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.
@article{arxiv.2305.15993,
title = {PoCaPNet: A Novel Approach for Surgical Phase Recognition Using Speech and X-Ray Images},
author = {Kubilay Can Demir and Tobias Weise and Matthias May and Axel Schmid and Andreas Maier and Seung Hee Yang},
journal= {arXiv preprint arXiv:2305.15993},
year = {2023}
}