ArticleIEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation2020
Autonomously Navigating a Surgical Tool Inside the Eye by Learning from Demonstration.
Article in IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Artificial Intelligence in Ophthalmology: Current Status, Challenges, and Future Perspectives.Health care science · 2026Review
- Toward comprehensive real-time scene understanding in ophthalmic surgery through multimodal image fusion.International journal of computer assisted radiology and surgery · 2026Article
- Will your next surgeon be a robot? Autonomy and AI in robotic surgery.Science robotics · 2025Review
- Embodied artificial intelligence in ophthalmology.NPJ digital medicine · 2025Review
- Microsurgery Robots: Applications, Design, and Development.Sensors (Basel, Switzerland) · 2023Review
- Simultaneous Online Registration-Independent Stiffness Identification and Tip Localization of Surgical Instruments in Robot-assisted Eye Surgery.IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society · 2023Article
- Robotic Assistance for Intraocular Microsurgery: Challenges and Perspectives.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2022Article
- Towards Autonomous Eye Surgery by Combining Deep Imitation Learning with Optimal Control.Proceedings of machine learning research · 2021Article
- Autonomous Needle Navigation in Retinal Microsurgery: Evaluation inIEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and AutomationArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
A fundamental challenge in retinal surgery is safely navigating a surgical tool to a desired goal position on the retinal surface while avoiding damage to surrounding tissues, a procedure that typically requires tens-of-microns accuracy. In practice, the surgeon relies on depth-estimation skills to localize the tool-tip with respect to the retina in order to perform the tool-navigation task, which can be prone to human error. To alleviate such uncertainty, prior work has introduced ways to assist the surgeon by estimating the tool-tip distance to the retina and providing haptic or auditory feedback. However, automating the tool-navigation task itself remains unsolved and largely unexplored. Such a capability, if reliably automated, could serve as a building block to streamline complex procedures and reduce the chance for tissue damage. Towards this end, we propose to automate the tool-navigation task by learning to mimic expert demonstrations of the task. Specifically, a deep network is trained to imitate expert trajectories toward various locations on the retina based on recorded visual servoing to a given goal specified by the user. The proposed autonomous navigation system is evaluated in simulation and in physical experiments using a silicone eye phantom. We show that the network can reliably navigate a needle surgical tool to various desired locations within 137
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.