Sebastian Weberskirch

Papers

Total Citations

H-Index

About

Sebastian Weberskirch is a pioneering researcher at the intersection of surgical oncology and artificial intelligence, with a primary focus on improving outcomes in esophageal cancer surgery. His work centers on intraoperative perfusion assessment, particularly through the innovative use of indocyanine green (ICG) fluorescence imaging during robotic esophagectomy. Weberskirch’s major contribution lies in developing an unsupervised learning approach that quantifies ICG dynamics, enabling objective, real-time evaluation of gastric conduit perfusion—a critical factor in preventing anastomotic leakage. This breakthrough addresses a long-standing challenge in minimally invasive esophagectomy, where subjective visual assessments have proven unreliable. His most-cited paper (2025, 2 citations) demonstrates the clinical potential of this AI-driven method, which has already garnered attention for its ability to standardize perfusion analysis. Though early in his career, Weberskirch’s work represents a significant step toward integrating machine learning into surgical decision-making, with implications for reducing postoperative complications. His research bridges computational methods and clinical practice, positioning him as a rising voice in precision surgery and the future of data-driven intraoperative care.