Robotic surgery & AI
Robotic surgery in visceral and hepatobiliary oncology is increasingly appreciated as therapeutic option of choice. Benefits of robotic surgery compared to other classical minimal invasive surgeries have been well documented, including reduced post-surgery pain, less blood loss, fewer and smaller scars. Most importantly, the automated arm movements allow a surgery with very high precision, balancing tremor movements and fine-tuned actions. Results include faster recovery for the patient, less risk of post-surgery complications (such as infections) as well as shorter overall hospital stays. Our work aims to improve minimal invasive surgery of cancer patients.
Dye and peptide-based labeling strategies of cancers that are located in-challenging-to-operate areas (such as rectum or gall bladder), as well as early detection of metastatic sites. This work is in cooperation with STIMULATE campus as well as Institute of Nanoporous and Nanoscale Materials, Natural Science Faculty of Heinrich-Heine University Düsseldorf.
Avoiding the use of labeling agents, we work on the generation of Raman spectroscopy-based molecular diagnostics of cancer cells. Raman-supported surgery robots may lead to improved operation performance or help to inform on disease characteristics to tailor adjuvant therapeutic interventions. This work is in cooperation with Institute of Pharmacy at Natural Science Faculty of Heinrich-Heine University Düsseldorf and Ramanservice GmbH.
Improved early detection through longitudinal surveillance of potential tumor recurrence via surgical-placed implant at the site of the primary tumor or metastatic organ. Device development, based on utilizing alterations in cell metabolism of cancer cells compared to non-transformed cells, comprises energy storage, light source and signal sensor. This work is in cooperation with STIMULATE Campus and the Clinic for Neuro- and Spine Surgery at Medical Faculty of Heinrich-Heine University Düsseldorf. We are supported by Volkswagen Foundation.
Artificial intelligence (AI)
Computational annotating data files of cellular behavior, such as growth rate or stress resistance, with optical data on morphology or growth pattern or genomic data, is emerging as a new technology for product development or diagnostics. Focusing on functional properties of primary tumor cells derived from surgical resections, in annotation with the respective clinical data of the patient, we apply machine-learning strategies to develop AI-based cancer diagnostics. In a midterm goal we aim to implement developed algorithms with data processing of operation robots to improve surgical decision-making and operation speed. Prof. Croner and Prof. Kahlert are Editorial Board members of the scientific journal Artificial Intelligence Surgery.