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Related Experiment Video

Updated: Jun 9, 2025

Retinal Pigment Epithelium Transplantation in a Non-human Primate Model for Degenerative Retinal Diseases
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Exploring the Needle Tip Interaction Force with Retinal Tissue Deformation in Vitreoretinal Surgery.

Simon Pannek1,2, Shervin Dehghani1, Michael Sommersperger1

  • 1Department of Computer Science, Technische Universität München, München 85748 Germany.

IEEE International Conference on Robotics and Automation : ICRA : [Proceedings]. IEEE International Conference on Robotics and Automation
|October 25, 2024
PubMed
Summary

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This study introduces a new dataset combining robotic surgery force data with optical coherence tomography (OCT) images for age-related macular degeneration treatments. This resource aids in developing algorithms for safer, tremor-free retinal surgeries.

Area of Science:

  • Ophthalmology
  • Robotics
  • Biomedical Engineering

Background:

  • Minimally invasive subretinal delivery for age-related macular degeneration (AMD) treatments requires high precision.
  • Surgeon hand tremors pose a risk to delicate retinal procedures, necessitating advanced solutions.
  • Robotic systems with force feedback show promise for enhancing surgical safety and precision.

Purpose of the Study:

  • To address the need for data to develop algorithms for robotic-assisted retinal surgery.
  • To introduce a novel dataset integrating force sensing and OCT imaging for subretinal interventions.
  • To demonstrate the utility of the dataset for developing image-based force estimation models.

Main Methods:

  • Development of a specialized experimental setup integrating robotic assistance and OCT microscopy.
Keywords:
Data Sets for Robot LearningData Sets for Robotic VisionMedical Robots and Systems

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Last Updated: Jun 9, 2025

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  • Acquisition of synchronized force data from FBG sensors and OCT B-scan images during simulated retinal procedures.
  • Creation of a unique dataset combining high-resolution imaging with precise force measurements.

    • Main Results:

    • A novel, synchronized dataset of force feedback and OCT imaging for subretinal surgery is presented.
    • A neural network model for image-based force estimation was developed and validated using the dataset.
    • The dataset enables the development of algorithms for tremor reduction and enhanced surgical control.

    Conclusions:

    • The presented dataset is crucial for advancing robotic-assisted retinal surgery by providing essential force and imaging data.
    • The developed neural network model demonstrates the practical application of the dataset for real-time surgical guidance.
    • This work facilitates the creation of more sophisticated algorithms for safer and more effective AMD treatments.