Computer Assisted Surgery

The Surgical Data Science (SDS) aims to enhance the quality of surgical interventions and improve surgical training through the comprehensive capture, organization, analysis, and modeling of surgical data.

An estimated 7 million surgeries worldwide annually are associated with avoidable adverse events (AE). These AEs can be attributed to two main factors. First, the lack of technical surgical skills, such as suboptimal use of surgical instruments, improper sequencing of actions or steps, deviations from established protocols, and more. Second, the lack of non-technical skills, including team miscommunication, inefficient team management, and stress control.

This call for papers focuses on the utilization of Surgical Data Science to revolutionize surgical practice. We invite submissions presenting innovative methods, concepts, or applications that are capable of or necessary for improving surgical training or enhancing the quality of surgical interventions. The scope of the call includes, but is not limited to, the following areas:

• Surgical knowledge representation (Ontologies)
• Surgical scene understanding
• Surgical navigation
• Surgical technical/non-technical skill analysis and assessment
• Surgical workflow recognition
• Instrument/anatomy detection, recognition and segmentation
• Multimodal/physiological sensors

We encourage researchers, practitioners, and experts in the field of Surgical Data Science to submit their original contributions and share their insights, findings, and advancements in this rapidly evolving domain. Any article types consistent with the journal's author guidelines are welcomed.

Keywords: Surgical Data Science, Machine Learning, Deep Learning, Situation Awareness, Surgical Training.

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Guest Advisors:

Arnaud Huaulmé, Ph.D. is a Research Engineer at the MediCIS research group from UMR 1099 LTSI, INSERM research institute, and the University of Rennes (France). His research topics are surgical data science and AI in surgery, including surgical robotics, augmented and virtual reality, surgical procedures and processes modeling, sensor-based surgical expertise assessment, surgical workflow recognition, surgical training, and validation methodology in medical image processing. He was the lead organizer of two MICCAI challenges, as part of EndoVis, focused on automatic workflow recognition (MISAW,2020 and PETRAW, 2021). He was involved in IPCAI 2023 organization as Area Chair. https://medicis.univ-rennes1.fr/members/arnaud.huaulme/index

Qi Dou, Ph.D. is an Assistant Professor with the Department of Computer Science & Engineering, and co-affiliated with T Stone Robotics Institute, at The Chinese University of Hong Kong. Her research interest lies in the interdisciplinary area of artificial intelligence and healthcare with expertise in medical image analysis and robot-assisted surgery. She served as the Program Co-Chair of IPCAI 2023, MICCAI 2022, MIDL 2021. The link to personal webpage is: https://www.cse.cuhk.edu.hk/~qdou/

Gernot Kronreif, Ph.D. is the Scientific Director at the “Austrian Center for Medical Innovation and Technology” (ACMIT), which is active in the area of translational R&D in medical technology. Main research interests are in medical robotics, surgical planning and navigation - and in particular the transfer of such technologies from the laboratory bench to the operating theatre. Since April 2022, Dr. Kronreif is also leading a 4-year international project in the area of Surgical Data Science and its application for optimized therapy setups. Company homepage: www.acmit.at

Disclosure Statement: Guest advisors have no conflicts of interest to disclose.

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All manuscripts submitted to this Article Collection will undergo a full peer-review; the Guest Advisor for this Collection will not be handling the manuscripts (unless they are an Editorial Board member).

Please review the journal scope and author submission instructions prior to submitting a manuscript.

The deadline for submitting manuscripts is July 31st, 2024.

Please contact Ruby Ru at [email protected] with any queries and discount codes regarding this Article Collection.

To submit your papers to this Article Collection, please:

1. Check "yes" for the question, "Are you submitting your paper for a specific special issue or article collection?"
2. Select the relevant Article Collection from the drop-down menu under the question, "Special Issue or Article Collection Name."

Instructions for AuthorsSubmit an Article