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Augmented reality self-training system for suturing in open surgery: A randomized controlled trial.

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Related Experiment Video

Updated: Nov 9, 2025

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A Novel Suture Training System for Open Surgery Replicating Procedures Performed by Experts Using Augmented Reality.

Yuri Nagayo1, Toki Saito2, Hiroshi Oyama2

  • 1Department of Clinical Information Engineering, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan. ynagayo@m.u-tokyo.ac.jp.

Journal of Medical Systems
|April 8, 2021
PubMed
Summary

This study introduces a new augmented reality (AR) suture training system. The system provides 3D visualization of expert surgical procedures, enabling trainees to self-practice open surgery skills effectively.

Keywords:
Augmented realityMotion captureOpen surgeryReplicationSimulation-based trainingSurgical training

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Area of Science:

  • Surgical Education
  • Medical Simulation
  • Augmented Reality in Medicine

Background:

  • The surgical training landscape is evolving due to work hour restrictions and safety concerns, driving the need for advanced simulation-based training.
  • Current low-fidelity simulators lack instructional feedback, limiting their effectiveness for teaching complex open surgical procedures.
  • Trainees often rely on insufficient self-study materials like textbooks or videos for practicing surgical skills.

Purpose of the Study:

  • To develop an innovative suture training system for open surgery.
  • To provide trainees with 3D visualization of expert surgical procedures for self-practice.
  • To enable trainees to observe and imitate expert techniques using augmented reality (AR).

Main Methods:

  • Developed a system combining a motion capture system for surgical instruments and a 3D replication system for procedures.
  • Utilized inexpensive cylindrical augmented reality (AR) markers for motion capture of surgical instruments.
  • Implemented AR visualization to display captured procedures in 3D, matching the position and orientation of the actual surgical field.

Main Results:

  • The proposed system successfully enabled users to observe expert procedures from any angle for a subcuticular interrupted suture.
  • Users could effectively imitate expert techniques by manipulating actual surgical instruments during self-practice.
  • The system provides 3D spatial and temporal information of expert surgical procedures.

Conclusions:

  • The developed AR-based training system offers a novel method for surgical skill acquisition.
  • This system facilitates self-directed learning of surgical techniques, particularly sutures, in the absence of expert supervision.
  • It is expected to enhance surgical training by providing immersive and interactive practice opportunities.