Call for papers - Deep learning applications in surgery

Guest Editor

Daichi Kitaguchi, MD, PhD, National Cancer Center Hospital East, Japan

Submission Status: Open | Submission Deadline: 23 June 2025

BMC Surgery is calling for submissions to our collection, Deep learning applications in surgery. This Collection seeks to gather cutting-edge research on the integration of deep learning and artificial intelligence in surgical practice. We invite submissions that explore the diverse applications of deep learning techniques in surgery, with a focus on enhancing surgical precision, optimizing patient outcomes, and revolutionizing the delivery of surgical care.

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Meet the Guest Editor

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Daichi Kitaguchi, MD, PhD, National Cancer Center Hospital East, Japan

Daichi Kitaguchi is an attending surgeon in the department of colorectal surgery at the National Cancer Center Hospital East, and an assistant professor in the department of surgery at the University of Tsukuba, Japan. His clinical focus is minimally invasive colorectal surgery, and his research focuses on applying artificial intelligence-based computer vision to surgery.

About the Collection

BMC Surgery is calling for submissions to our collection, Deep learning applications in surgery.

The integration of deep learning and artificial intelligence (AI) in surgical practice has emerged as a transformative area of research and innovation. Deep learning techniques, utilizing multi-layered neural networks and machine learning algorithms, are being increasingly applied to various aspects of surgical care, ranging from preoperative planning to intraoperative decision-making, and postoperative monitoring. These technologies have the potential to simulate the complex decision-making power of the human brain to aid surgical precision, optimize patient outcomes, and revolutionize the delivery of surgical care.

Recent advances have demonstrated the potential of deep learning algorithms in image analysis, predictive modeling, and real-time guidance during surgical procedures. These developments have paved the way for personalized surgical approaches, improved diagnostic accuracy, and enhanced patient safety. Continued advancement in our collective understanding of deep learning applications in surgery is crucial for driving the evolution of surgical innovation and technology.

We invite submissions from all aspects of this field, including, but not limited to:

Deep learning for image analysis in surgery
AI-driven predictive modeling in surgical care
Neural network applications in intraoperative decision support
Machine learning for personalized surgical approaches
AI-assisted transitioning to minimally invasive surgery

Looking ahead, ongoing research in this field holds the promise of further refining deep learning models for surgical applications, enabling the development of autonomous surgical systems, and facilitating the integration of AI-driven decision support tools into routine surgical practice. Additionally, the exploration of deep learning applications in surgical education and training is anticipated to shape the future landscape of surgical skill development and proficiency assessment.

There are currently no articles in this collection.

Submission Guidelines

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This Collection welcomes submission of original Research Articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select "Deep learning applications in surgery" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.