Felix von Bechtolsheim

About

Felix von Bechtolsheim is a rising researcher at the intersection of artificial intelligence, surgical simulation, and medical education. His work focuses on leveraging technology to improve surgical training, particularly in minimally invasive and robot-assisted surgery. A key contribution is his pioneering use of 3D convolutional neural networks to automatically recognize surgical gestures from video, a technique that promises to enable objective, data-driven assessment of surgical skill. His research also critically examines the role of force feedback in learning suturing, questioning how its delivery modality impacts performance. Demonstrating a commitment to evidence-based curriculum design, he led a modified Delphi study to establish a national German curriculum for minimally invasive and robotic surgery (GeRMIQ). His work shows that virtual reality training can sufficiently develop tissue handling skills compared to real robotic systems. With over 37 citations across his most-cited works, von Bechtolsheim is shaping the future of surgical education by integrating computer vision, haptics, and rigorous pedagogical research.

Research Focus

Key Achievements

Top Papers

1. 1
   Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video

   13 citations · 2019
2. 2
   How does the modality of delivering force feedback influence the performance and learning of surgical suturing skills? We don’t know, but we better find out! A review

   11 citations · 2022
3. 3
   The development of tissue handling skills is sufficient and comparable after training in virtual reality or on a surgical robotic system: a prospective randomized trial

   9 citations · 2024
4. 4
   Modifiziertes Delphi-Verfahren zur Konsensfindung für die Konzeption eines bundesweiten Curriculums für minimalinvasive und roboterassistierte Chirurgie in Deutschland (GeRMIQ)

   4 citations · 2024

Key Collaborators

Contact & Links