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Related Experiment Video

Updated: Dec 31, 2025

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Three-dimensional posture estimation of robot forceps using endoscope with convolutional neural network.

Takuto Mikada1, Takahiro Kanno1, Toshihiro Kawase1,2

  • 1Department of Biomechanics, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, Tokyo, Japan.

The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS
|January 9, 2020
PubMed
Summary

This study introduces a new method using convolutional neural networks (CNNs) to estimate surgical forceps posture from endoscopic images. This image-based sensing approach is effective and more accurate than human detection limits.

Keywords:
machine learningposture estimationsurgical robot

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Area of Science:

  • Robotics
  • Computer Vision
  • Surgical Technology

Background:

  • Advancements in surgical robotics necessitate innovative sensing solutions.
  • Image-based sensing offers advantages for robot hygiene, avoiding electric sensors.

Purpose of the Study:

  • To develop and validate a method for estimating the 3D posture of surgical forceps tips using endoscopic imagery.
  • To enable sensorless posture estimation for enhanced robot design.

Main Methods:

  • A convolutional neural network (CNN) was employed to process endoscopic images of tracked markers on forceps.
  • The CNN model was trained to output the 3D posture of the forceps tip.

Main Results:

  • Posture estimation from images accurately correlated with electrical sensor data.
  • Calculated external forces based on estimated posture aligned with measured values.
  • The system demonstrated high precision, with external force error below human perception thresholds.

Conclusions:

  • CNN-based image analysis provides an effective method for surgical forceps posture estimation.
  • This approach facilitates the development of easily washable and sensorless surgical robots.
  • The accuracy of external force estimation surpasses human sensory capabilities.