SurgVisAgent: Multimodal Agentic Model for Versatile Surgical Visual Enhancement

• URL: http://arxiv.org/abs/2507.02252v1
• Date: Thu, 03 Jul 2025 03:00:26 GMT
• Title: SurgVisAgent: Multimodal Agentic Model for Versatile Surgical Visual Enhancement
• Authors: Zeyu Lei, Hongyuan Yu, Jinlin Wu, Zhen Chen,
• Abstract summary: SurgVisAgent is an end-to-end intelligent surgical vision agent built on multimodal large language models (MLLMs)<n>It dynamically identifies distortion categories and severity levels in endoscopic images, enabling it to perform a variety of enhancement tasks.<n>We construct a benchmark simulating real-world surgical distortions, on which extensive experiments demonstrate that SurgVisAgent surpasses traditional single-task models.
• Score: 8.337819078911405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precise surgical interventions are vital to patient safety, and advanced enhancement algorithms have been developed to assist surgeons in decision-making. Despite significant progress, these algorithms are typically designed for single tasks in specific scenarios, limiting their effectiveness in complex real-world situations. To address this limitation, we propose SurgVisAgent, an end-to-end intelligent surgical vision agent built on multimodal large language models (MLLMs). SurgVisAgent dynamically identifies distortion categories and severity levels in endoscopic images, enabling it to perform a variety of enhancement tasks such as low-light enhancement, overexposure correction, motion blur elimination, and smoke removal. Specifically, to achieve superior surgical scenario understanding, we design a prior model that provides domain-specific knowledge. Additionally, through in-context few-shot learning and chain-of-thought (CoT) reasoning, SurgVisAgent delivers customized image enhancements tailored to a wide range of distortion types and severity levels, thereby addressing the diverse requirements of surgeons. Furthermore, we construct a comprehensive benchmark simulating real-world surgical distortions, on which extensive experiments demonstrate that SurgVisAgent surpasses traditional single-task models, highlighting its potential as a unified solution for surgical assistance.

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