Felix Thielke

Fraunhofer Institute for Digital Medicine

Papers

Total Citations

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About

Felix Thielke is a researcher at the forefront of applying deep learning to surgical video analysis, with a primary focus on improving outcomes in robot-assisted radical prostatectomy (RARP) for prostate cancer. His work centers on developing predictive models from endoscopic footage to forecast critical patient outcomes, most notably the preservation of Early Urinary Continence (EUC). By leveraging deep learning to analyze surgical videos, Thielke aims to provide clinicians with real-time, data-driven insights that can inform surgical planning and enhance postoperative recovery. His most-cited paper, "Deep-learning-based outcome prediction from endoscopic videos for robot-assisted radical prostatectomy" (2025), has already garnered early citations, signaling growing interest in this translational application of AI in urology. This work represents a significant step toward integrating computer vision into the operating room, potentially reducing the variability in surgical outcomes and improving quality of life for prostate cancer patients. Thielke’s research bridges the gap between advanced machine learning techniques and practical clinical needs, making him a notable contributor to the emerging field of surgical AI.