Evidence map›Paper›PMID 41399488›Full record

ArticleProceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems2025

A Deep Learning-Driven Autonomous System for Retinal Vein Cannulation: Validation Using a Chicken Embryo Model.

Yi Wang, Peiyao Zhang, Mojtaba Esfandiari, Peter Gehlbach, Iulian I Iordachita

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yi WangYi Wang is with the Department of Robotics and also the Laboratory for Computational Sensing and Robotics at the Johns Hopkins University, Baltimore, MD, 21218, USA.
Peiyao ZhangPeiyao Zhang, Mojtaba Esfandiari, and Iulian Iordachita are with the Department of Mechanical Engineering and also the Laboratory for Computational Sensing and Robotics at the Johns Hopkins University, Baltimore, MD, 21218, USA.
Mojtaba EsfandiariPeiyao Zhang, Mojtaba Esfandiari, and Iulian Iordachita are with the Department of Mechanical Engineering and also the Laboratory for Computational Sensing and Robotics at the Johns Hopkins University, Baltimore, MD, 21218, USA.
Peter GehlbachPeter Gehlbach is with the Wilmer Eye Institute, Johns Hopkins Hospital, Baltimore, MD, 21287, USA.
Iulian I IordachitaPeiyao Zhang, Mojtaba Esfandiari, and Iulian Iordachita are with the Department of Mechanical Engineering and also the Laboratory for Computational Sensing and Robotics at the Johns Hopkins University, Baltimore, MD, 21218, USA.

Funding

Enabling Technology for Safe Robot-assisted Surgical MicromanipulationR01EB023943 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI GEHLBACH, PETER LOUIS, IORDACHITA, IULIAN IOAN · 2017 to 2024
$2.3M
Enabling technology for image-guided robot-assisted sub-retinal injectionsR01EB025883 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI IORDACHITA, IULIAN IOAN · 2019 to 2021
$1.5M
Enabling Dexterous Intraocular Surgery with Robotic AssistanceR01EB034397 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI IULIAN IOAN IORDACHITA · 2024 to 2026
$1.2M
NIBIB NIH HHS R01 EB023943NIBIB NIH HHS R01 EB025883NIBIB NIH HHS R01 EB034397
6 · The paper itself

Abstract

Retinal vein cannulation (RVC) is a minimally invasive microsurgical procedure for treating retinal vein occlusion (RVO), a leading cause of vision impairment. However, the small size and fragility of retinal veins, coupled with the need for high-precision, tremor-free needle manipulation, create significant technical challenges. These limitations highlight the need for robotic assistance to improve accuracy and stability. This study presents an automated robotic system with a top-down microscope and B-scan optical coherence tomography (OCT) imaging for precise depth sensing. Deep learning-based models enable real-time needle navigation, contact detection, and vein puncture recognition, using a chicken embryo model as a surrogate for human retinal veins. The system autonomously detects needle position and puncture events with 85% accuracy. The experiments demonstrate notable reductions in navigation and puncture times compared to manual methods. Our results demonstrate the potential of integrating advanced imaging and deep learning to automate microsurgical tasks, providing a pathway for safer and more reliable RVC procedures with enhanced precision and reproducibility.

Identifiers

PMID41399488
PMCPMC12702478

What OpenQuestion holds

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Registered trials

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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.