Bhargav Ghanekar
Papers
1
Total Citations
2
H-Index
1
About
Bhargav Ghanekar is a researcher at the forefront of applying deep learning to surgical robotics and computer vision. His work centers on developing intelligent systems that can understand and analyze complex visual data, with a particular focus on video-based surgical tool tracking. Ghanekar’s major contribution lies in advancing multi-frame context-driven deep learning models to automatically detect and track surgical tool-tips and keypoints in robotic surgery videos. This innovation is critical for enabling downstream applications such as automated skill assessment, expertise evaluation, and the delineation of safety zones during procedures. His most-cited paper, “Video-Based Surgical Tool-Tip and Keypoint Tracking Using Multi-Frame Context-Driven Deep Learning Models” (2025), has already garnered 2 citations, signaling early impact in a rapidly evolving field. By addressing the challenges of real-time, precise tracking in minimally invasive surgery, Ghanekar is helping to pave the way for safer, more efficient robotic-assisted operations. His work bridges the gap between computer vision and clinical practice, offering tools that could transform surgical training and patient outcomes.
Research Focus
Key Achievements
Top Papers
- 1