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Deep Learning-Enhanced Robotic Subretinal Injection with Real-Time Retinal Motion Compensation.

Tianle Wu1, Mojtaba Esfandiari1, Peiyao Zhang1

  • 1Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA.

IEEE International Conference on Automation Science and Engineering (CASE) : [Proceedings]. IEEE Conference on Automation Science and Engineering
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered robotic system for subretinal injections, improving precision in treating retinal diseases. It synchronizes needle and retinal motion using deep learning, enhancing surgical safety and accuracy.

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Area of Science:

  • Ophthalmology
  • Robotics
  • Artificial Intelligence

Background:

  • Subretinal injection is crucial for treating retinal diseases like inherited retinal diseases (IRD) and age-related macular degeneration (AMD).
  • Physiological retinal motion poses challenges for precise needle placement, risking retinal pigment epithelium (RPE) damage.

Purpose of the Study:

  • To develop a fully autonomous robotic system for subretinal injection.
  • To enhance surgical precision and safety by synchronizing needle and retinal motion.

Main Methods:

  • Integration of intraoperative optical coherence tomography (iOCT) imaging and deep learning for motion prediction.
  • Utilizing a Long Short-Term Memory (LSTM) neural network to predict internal limiting membrane (ILM) motion.
  • Implementation of a real-time registration framework and dynamic proportional speed control for needle insertion.

Main Results:

  • The LSTM model outperformed a Fast Fourier Transform (FFT) baseline in predicting ILM motion.
  • Experimental validation in simulations and ex vivo porcine eyes demonstrated precise motion synchronization.
  • Achieved a mean tracking error below 16.4 μm in pre-insertion phases.

Conclusions:

  • The AI-driven robotic system shows significant potential for improving the safety and accuracy of retinal microsurgery.
  • Autonomous robotic assistance can overcome challenges posed by physiological retinal motion.