Luca Zappella

Papers

Total Citations

627

H-Index

About

Luca Zappella is a leading researcher in surgical data science, with a primary focus on the automated analysis of surgical gestures and robotic surgery. His work bridges computer vision and robotics to improve surgical skill assessment and operational efficiency. Zappella’s major contributions include pioneering the use of video and kinematic data for surgical gesture classification, segmentation, and recognition—enabling machines to understand and evaluate complex surgical maneuvers. His landmark 2017 paper, "A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery," has garnered 288 citations and established a standardized framework for comparing algorithms in the field. Earlier foundational works, such as "Surgical gesture classification from video and kinematic data" (152 citations) and "Surgical Gesture Segmentation and Recognition" (127 citations), have collectively shaped modern approaches to automated surgical analysis. With over 600 total citations, Zappella’s research has significantly advanced the reproducibility and objectivity of surgical skill assessment, making him a key figure in the integration of AI into operating rooms. His work continues to inspire new methods for training surgeons and enhancing robotic-assisted procedures.