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Intelligent Systems and Robotics in Urology

Junior Clinical Cooperation Unit Intelligent Systems and Robotics in Urology

PD Dr. Karl-Friedrich Kowalewski

Traditionally, surgical disciplines like Urology have relied on surgeons' intraoperative situational awareness and their interpretations of the surgical field and anatomy. However, recent advances in artificial intelligence and its subdomains have opened up exciting possibilities for real-time analysis of vast amounts of data and video streams to assist the surgeon. Our research group is actively engaged in developing, validating, and translating these AI methods into practical clinical care.
To harness this potential, we gather and analyze information accumulated from various devices during surgeries. After thorough processing by means of segmentation and annotation, valuable surgical datasets are created. These datasets serve as the foundation for creating and testing models that offer surgical assistance and decision-making support.

One additional focus of our research lies in augmented intraoperative tissue assessment, where we employ cutting-edge biophotonics technologies such as hyperspectral imaging and fluorescence. These techniques enable real-time analysis of oxygen saturation and perfusion, empowering surgeons with enhanced abilities to evaluate tissue properties on the spot and make well-informed decisions during surgery.

However, the integration of these advanced techniques into daily surgical practice is still limited due to the lack of high-level evidence supporting their effectiveness. To overcome this barrier, our research group is committed to conducting large-scale clinical trials to rigorously test these technologies. This will enable safe and meaningful implementation of intelligent systems into routine care for more precise oncological surgery. This will ultimately benefit both patients and caregivers, ensuring better outcomes and improved healthcare services.


PD Dr. Karl-Friedrich Kowalewski
Intelligent Systems and Robotics in Urology (E140)

Deutsches Krebsforschungszentrum
Forschungszentrum für Bildgebung und Radioonkologie
Im Neuenheimer Feld 223
69120 Heidelberg

E-Mail: Karl-friedrich.kowalewski (at)

Selected Publications

  • Kowalewski KF, Garrow CR, Schmidt MW, Benner L, Müller-Stich BP, Nickel F (2019) Sensor-based machine learning for workflow detection and as key to detect expert level in laparoscopic suturing and knot-tying Surg Endosc. 33(11):3732-3740
  • Kowalewski KF, Hendrie JD, Schmidt MW, Garrow CR, Bruckner T, Proctor T, Paul S, Adigüzel D, Bodenstedt S, Erben A, Kenngott H, Erben Y, Speidel S, Müller-Stich BP, Nickel F. Development and validation of a sensor- and expert model-based training system for laparoscopic surgery. Surg Endosc. 2017 May;31(5):2155-2165 doi: 10.1007/s00464-016-5213-2. Epub 2016 Sep 7. PMID: 27604368.
  • Garrow CR*, Kowalewski KF*, Li L, Wagner M, Schmidt MW, Engelhardt S, Hashimoto DA, Kenngott HG, Bodenstedt S, Speidel S, Müller-Stich BP, Nickel F (2021); *Shared-first ; Machine Learning for Surgical Phase Recognition: A Systematic Review; Ann Surg. 273(4):684-693
  • Kowalewski, K. F., Neuberger, M., Sidoti Abate, M. A., Kirchner, M., Haney, C. M., Siegel, F., Westhoff, N., Michel, M. S., Honeck, P., Nuhn, P. und Kriegmair, M. C. (2023). Randomized Controlled Feasibility Trial of Robot-assisted Versus Conventional Open Partial Nephrectomy: The ROBOCOP II Study. Eur Urol Oncol, doi: 10.1016/j.euo.2023.05.011
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