Benchmarking and Enhancing Surgical Phase Recognition Models for Robotic-Assisted Esophagectomy

• URL: http://arxiv.org/abs/2412.04039v1
• Date: Thu, 05 Dec 2024 10:23:16 GMT
• Title: Benchmarking and Enhancing Surgical Phase Recognition Models for Robotic-Assisted Esophagectomy
• Authors: Yiping Li, Romy van Jaarsveld, Ronald de Jong, Jasper Bongers, Gino Kuiper, Richard van Hillegersberg, Jelle Ruurda, Marcel Breeuwer, Yasmina Al Khalil,
• Abstract summary: Robotic-assisted minimally invasive esophagectomy (RAMIE) is a recognized treatment for esophageal cancer. Our goal is to leverage deep learning for surgical phase recognition in RAMIE to provide intraoperative support to surgeons. To more effectively capture the temporal dynamics of this complex procedure, we developed a novel deep learning model featuring an encoder-decoder structure with causal hierarchical attention.
• Score: 1.0807134580166777
• License:
• Abstract: Robotic-assisted minimally invasive esophagectomy (RAMIE) is a recognized treatment for esophageal cancer, offering better patient outcomes compared to open surgery and traditional minimally invasive surgery. RAMIE is highly complex, spanning multiple anatomical areas and involving repetitive phases and non-sequential phase transitions. Our goal is to leverage deep learning for surgical phase recognition in RAMIE to provide intraoperative support to surgeons. To achieve this, we have developed a new surgical phase recognition dataset comprising 27 videos. Using this dataset, we conducted a comparative analysis of state-of-the-art surgical phase recognition models. To more effectively capture the temporal dynamics of this complex procedure, we developed a novel deep learning model featuring an encoder-decoder structure with causal hierarchical attention, which demonstrates superior performance compared to existing models.

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