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Related Experiment Video

Updated: Oct 15, 2025

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
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Towards Autonomous Eye Surgery by Combining Deep Imitation Learning with Optimal Control.

Ji Woong Kim1, Peiyao Zhang1, Peter Gehlbach2

  • 1Department of Mechanical Engineering, Johns Hopkins University.

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|October 29, 2021
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Summary

This study introduces an AI framework for automated retinal microsurgery tool navigation. The system accurately guides surgical tools, surpassing human surgeon precision and minimizing tissue damage for improved outcomes.

Keywords:
deep imitation learningeye surgeryophthalmologyoptimal control

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Area of Science:

  • Ophthalmology
  • Robotics
  • Computer Vision

Background:

  • Retinal microsurgery demands high precision, yet depth perception is limited by the top-down surgical view.
  • Navigating surgical tools in the constrained retinal workspace is challenging for surgeons.

Purpose of the Study:

  • To automate surgical tool navigation in retinal microsurgery.
  • To enhance surgical precision and minimize tissue damage through AI-driven trajectory planning.

Main Methods:

  • Developed an AI framework to predict relative goal positions on the retina from the current tool-tip position.
  • Generated optimal, safety-constrained trajectories for tool navigation.
  • Integrated eye geometry localization for comprehensive surgical planning.

Main Results:

  • Achieved navigation accuracy within 0.089mm and 0.118mm xy error, outperforming human surgeon tremor (0.180mm).
  • Successfully fulfilled all safety constraints, ensuring minimal tissue damage.
  • Demonstrated robustness across simulations, eye phantoms, and novel scenarios.

Conclusions:

  • The proposed AI framework significantly enhances precision and safety in retinal microsurgery.
  • Automated tool navigation shows potential to improve surgical outcomes and reduce risks.