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Related Concept Videos

Three-Dimensional Force System01:30

Three-Dimensional Force System

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In mechanical engineering, a three-dimensional force system is a system of forces acting in three dimensions, with forces applied along the x, y, and z coordinate axes. The three-dimensional force system is an important concept in mechanical engineering, as it allows engineers to understand and analyze the behavior of objects and structures in three dimensions. By understanding the forces acting on a system, engineers can design more efficient and effective mechanical systems that can withstand...
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Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
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Related Experiment Video

Updated: Aug 14, 2025

Direct Force Measurements of Subcellular Mechanics in Confinement using Optical Tweezers
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Optical force estimation for interactions between tool and soft tissues.

Maximilian Neidhardt1, Robin Mieling2, Marcel Bengs1

  • 1Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology, Am Schwarzenberg-Campus 3, Hamburg, 21073, Germany.

Scientific Reports
|January 10, 2023
PubMed
Summary
This summary is machine-generated.

Accurately estimating forces in robotic surgery is difficult. This study uses optical coherence tomography and deep learning to estimate forces by analyzing tissue deformation and stiffness, improving accuracy for minimally invasive procedures.

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Area of Science:

  • Biomedical Engineering
  • Surgical Robotics
  • Medical Imaging

Background:

  • Minimally invasive surgery (MIS) utilizes robotic assistance for enhanced precision and patient outcomes.
  • A significant limitation in robotic surgery is the absence of haptic (force) feedback, hindering precise tool-tissue interaction assessment.
  • Current image-based force estimation methods often overlook tissue heterogeneity, impacting accuracy.

Purpose of the Study:

  • To develop an accurate, sensorless method for estimating tool-tissue interaction forces in robotic surgery.
  • To address the challenge of heterogeneous tissue properties in image-based force estimation.
  • To integrate deformation and local tissue elasticity measurements for improved force prediction.

Main Methods:

  • Utilized optical coherence tomography (OCT) to simultaneously capture tissue deformation and perform shear wave elastography.
  • Developed a multi-input deep learning network to process OCT-derived volumetric image data and local elasticity estimates.
  • Validated the approach using phantom studies and ex vivo soft tissue samples with varying properties.

Main Results:

  • Accounting for local tissue elastic properties is crucial for accurate image-based force estimation across diverse tissue types.
  • Joint processing of deformation and elasticity data significantly enhanced force estimation performance in phantom studies.
  • The developed deep learning model demonstrated generalization capabilities to untrained soft tissue samples.

Conclusions:

  • Image-based force estimation in robotic surgery can be significantly improved by incorporating local tissue elasticity information.
  • The combined OCT and deep learning approach offers a promising, sensorless solution for haptic feedback in MIS.
  • This method has the potential to enhance surgeon control and patient safety in robotic-assisted procedures.