Max Robotics — Global Service Robot Directory + US Certification

About

Shubhangi Nema is a rising researcher at the intersection of computer vision and robot-assisted surgery, whose work is pioneering the automation of surgical skill assessment. Her primary research areas include surgical instrument detection, segmentation, and path planning, all aimed at enhancing intraoperative assistance and surgical training. Nema’s most impactful contribution is her 2022 paper on "Surgical instrument detection and tracking technologies," which has garnered 34 citations for its novel approach to automating dataset labeling—a critical bottleneck in developing systems that differentiate surgeon skill levels through instrument motion analysis. She further advanced the field with her 2023 work on "Unpaired deep adversarial learning for multi-class segmentation," addressing the challenge of segmenting instruments occluded by overlays in surgical videos, a key step toward robust tracking frameworks. Additionally, her 2021 paper on "Safe and Fast Path Planner for Minimally Invasive Surgery" tackles the complex problem of planning tool paths for non-uniform instruments, offering solutions for faster, safer movements during procedures. Through these contributions, Nema is shaping the future of surgical data science, providing tools that promise to reduce adverse outcomes and improve training feedback for surgeons worldwide.