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International Journal of Computer Assisted Radiology and Surgery

A deep learning-driven method for safe and effective ERCP cannulation

  • 07-02-2025
  • Original Article
Published in:
Authors
Yuying Liu
Xin Chen
Siyang Zuo
Published in
International Journal of Computer Assisted Radiology and Surgery | Issue 5/2025

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Abstract

The article focuses on a deep learning-driven method for safe and effective ERCP cannulation, addressing the complexities of precise cannulation in a dynamic surgical environment. It introduces the 4STDH framework, which leverages four prediction heads and a swin transformer module to detect duodenal papilla and surgical cannula with high accuracy. The study also presents the DPAC dataset, specifically designed for ERCP cannulation guidance, which has been annotated under clinical expert guidance. Experimental results show that the 4STDH method outperforms state-of-the-art algorithms in terms of accuracy and robustness, making it a valuable tool for enhancing the safety and efficiency of ERCP procedures. The article concludes by highlighting the potential of the proposed method to improve clinical outcomes and serve as a training tool for medical professionals.
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previous article Depth-based registration of 3D preoperative models to intraoperative patient anatomy using the HoloLens 2
next article Automatic diagnosis of abdominal pathologies in untrimmed ultrasound videos
Title
A deep learning-driven method for safe and effective ERCP cannulation
Authors
Yuying Liu
Xin Chen
Siyang Zuo
Publication date
07-02-2025
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 5/2025
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-025-03329-w
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