Erica Padovan
Papers
1
Total Citations
37
H-Index
1
About
Erica Padovan is a leading researcher at the intersection of artificial intelligence and minimally invasive surgery, with a primary focus on computer vision, deep learning, and augmented reality for robot-assisted procedures. Her most cited work, "A deep learning framework for real‐time 3D model registration in robot‐assisted laparoscopic surgery" (2022, 37 citations), introduces a novel framework that infers the position and rotation of a target organ directly from endoscopic video, enabling the real-time overlay of a patient-specific 3D model onto the surgical field. This contribution addresses a critical challenge in surgical navigation—achieving accurate, low-latency registration without external trackers—and has been recognized as a foundational step toward safer, more intuitive augmented reality guidance in the operating room. Padovan’s research is distinguished by its practical, translational approach, bridging state-of-the-art deep learning with the stringent demands of clinical environments. Her work has garnered attention for its potential to reduce operative errors and improve outcomes in laparoscopic surgery, marking her as an emerging voice in surgical data science and computer-assisted intervention.
Research Focus
Key Achievements
Top Papers
- 1