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Related Experiment Video

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A data-driven soft sensor for needle deflection in heterogeneous tissue using just-in-time modelling.

Carlos Rossa1, Thomas Lehmann2, Ronald Sloboda3

  • 1Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2V4, Canada. rossa@ualberta.ca.

Medical & Biological Engineering & Computing
|December 13, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel local data-driven model for estimating needle deflection in soft tissues. The method accurately predicts needle tip 3D deflection and insertion force using real-time data.

Keywords:
Data-drivenJust-in-time modellingNeedle steeringSoft sensorSurgical needles

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Area of Science:

  • Robotics
  • Biomedical Engineering
  • Data Science

Background:

  • Traditional global modeling for needle deflection in soft tissue has limitations.
  • Accurate estimation of needle-tissue interaction is crucial for minimally invasive surgery.

Purpose of the Study:

  • To develop and validate a new local data-driven modeling approach for estimating needle deflection.
  • To improve the accuracy of predicting needle tip trajectory and insertion force during tissue manipulation.

Main Methods:

  • Collected external measurements of needle-tissue interactions from multiple insertions in ex vivo tissue.
  • Developed a just-in-time learning method using a historical database of insertion depth, rotations, forces, and torques.
  • Employed a local linear model fit to relevant historical data points for real-time prediction.

Main Results:

  • The model accurately predicted 3D needle tip deflection (0.81 mm horizontal, 1.24 mm vertical).
  • Achieved high accuracy in predicting needle insertion force (0.5 N).
  • Validated the approach across various needle insertion scenarios in multilayered biological tissue.

Conclusions:

  • The proposed local data-driven modeling method offers a significant advancement over traditional global modeling for needle deflection.
  • This approach provides accurate and real-time estimation of needle behavior, enhancing robotic-assisted surgery and medical procedures.
  • The method's validation demonstrates its potential for practical application in complex biological tissues.