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Related Experiment Video

Updated: May 15, 2026

A Spine Robotic-Assisted Navigation System for Pedicle Screw Placement
06:24

A Spine Robotic-Assisted Navigation System for Pedicle Screw Placement

Published on: May 11, 2020

Vision-based augmented reality guidance for setting up robot-assisted spine surgery.

Blanca Inigo1, Sungwoo Kim2, Sue Min Cho2

  • 1Johns Hopkins University, Baltimore, Maryland, USA. binigo1@jh.edu.

International Journal of Computer Assisted Radiology and Surgery
|May 13, 2026
PubMed
Summary

This study introduces a vision-based augmented reality (AR) system for robot-assisted surgery. It enables accurate spatial calibration using only RGB video, simplifying setup and improving needle alignment for robotic procedures.

Keywords:
Computer-assisted interventionImage-guided interventionsMixed realityRobotic navigationSpine surgery

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Last Updated: May 15, 2026

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Area of Science:

  • Medical Robotics
  • Computer-Assisted Surgery
  • Augmented Reality

Background:

  • Robot-assisted surgery enhances precision but faces setup challenges.
  • Augmented reality (AR) offers visual guidance but often requires complex calibration.
  • Current AR systems necessitate additional steps for head-mounted display and imaging system calibration.

Purpose of the Study:

  • To develop an AR guidance approach for robot-assisted procedures using only RGB video for spatial calibration.
  • To minimize setup effort and complexity in AR-guided robotic surgery.
  • To enable markerless AR guidance through efficient spatial calibration.

Main Methods:

  • Established a shared coordinate frame between AR headset and intraoperative imaging system.
  • Utilized a Vision Foundation Model (VFM) to estimate inter-device poses directly from RGB video.
  • Applied multi-frame Umeyama optimization with RANSAC for refining scale, rotation, and translation in challenging scenarios.

Main Results:

  • Achieved sub-3° rotation and 15 mm mean translation error compared to ArUco-based methods.
  • Demonstrated markerless calibration, eliminating the need for fiducial markers.
  • Verified accurate needle placement within the robot's workspace, allowing for fine adjustments.

Conclusions:

  • Vision-based calibration enables accurate, markerless AR guidance for robotic procedures.
  • The proposed approach significantly reduces setup complexity.
  • This method shows feasibility for seamless AR integration in robotic surgery.