Translational Surgical Oncology

Increasingly powerful technological developments in surgery like new devices and technologies provide a huge amount of valuable data which can be used to improve patient therapy. Although a lot of data is available, the numerous data sources are an overwhelming challenge for physicians, especially in the operating room.

We are an interdisciplinary research group including disciplines like computer science, engineering and medicine. Our research goal is to develop and evaluate computer- and robotic-assisted systems which optimize the surgical therapy of the individual patient by turning the available data into useful information.

Augmented Reality visualization by superimposing virtual images: Tumors (yellow), Vascular Tree (blue), Liver (red).

Therefore, we are investigating the entire process chain along the surgical treatment path including pre-, intra- and postoperative patient information. Eligibility and success rates of surgeries can be improved using personalized digital representations of the patient and enable the deployment of this data in the operating room (OR), e.g. by visualization of planning data or the prediction of complications.

The research and methodological focuses are surgical workflow analysis, soft-tissue navigation and intraoperative visualization as well as surgical training and surgical data science. These methods allow for the first time a context-aware assistance in the OR of the future, which acts as an automatic information filter, avoids information overflow, adapts to the current needs of the surgeon and therefore provides the right information at the right time. In order to achieve this goal the close collaboration with interdisciplinary partners, in particular physicians, is necessary. Overall such systems have the potential to improve patient outcome and open up new possibilities to operate.

Contact

Prof. Dr. Stefanie Speidel
Translational Surgical Oncology
Phone: +49 (0)351 458 5413
E-Mail: Stefanie.speidel(at)nct-dresden.de

Prof. Dr. Stefanie Speidel (Copyright: André Wirsig)

Own recent publications

M. Wagner, BP. Müller-Stich, A. Kisilenko, D. Tran, P. Heger, L. Mündermann,...L. Maier-Hein, S. Speidel, S. Bodenstedt: Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the heichole benchmark, Medical Image Analysis, 86, 102770, 2023

N. Bhasker, F. Schön, JP. Kühn, S. Speidel: Bildgebende Diagnostik und der Einsatz von künstlicher Intelligenz beim Management von Organmetastasen, Die Onkologie, 29, 182-191, 2023

FR. Kolbinger, FM. Rinner, AC. Jenke, M. Carstens…S. Speidel, S. Bodenstedt: Better than humans? Machine learning-based anatomy recognition in minimally-invasive abdominal surger, medRxiv, 13, 1-22, 2022

M. Wagner, JM. Brandenburg, S. Bodenstedt, A. Schulze, AC. Jenke, ...S. Speidel: Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data, Surgical Endoscopy, 36, 8568–8591, 2022

L. Oppici, K. Grütters, F. Bechtolsheim, S. Speidel: How does the modality of delivering force feedback influence the performance and learning of surgical suturing skills? We don’t know, but we better find out! A review, Surgical Endoscopy, 1–14, 2022

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Translational Surgical Oncology