Florian Oehme

Papers

Total Citations

H-Index

About

Florian Oehme is a pioneering researcher at the intersection of artificial intelligence and robotic surgery, with a primary focus on advancing surgical skill assessment and training methodologies. His landmark 2019 work, "Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video," introduced a novel deep learning approach that automatically identifies and classifies surgical gestures from video data—a critical step toward objective, data-driven surgical evaluation. This foundational paper has garnered 13 citations, establishing Oehme as a key contributor to computer vision applications in surgery. More recently, his 2024 prospective randomized trial, "The development of tissue handling skills is sufficient and comparable after training in virtual reality or on a surgical robotic system," directly addresses a pressing question in surgical education: whether VR training can adequately prepare surgeons for real robotic systems. With 9 citations already, this work provides compelling evidence that VR-based training achieves comparable tissue handling proficiency to training on actual robotic platforms, offering significant implications for cost-effective, scalable surgical education. Oehme’s research uniquely bridges machine learning and clinical training, demonstrating how automated video analysis can enhance both skill assessment and curriculum design.