Evidence map›Paper›PMID 40937043›Full record

ArticleIEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation2025

Real-time Deformation-aware Control for Autonomous Robotic Subretinal Injection under iOCT Guidance.

Demir Arikan, Peiyao Zhang, Michael Sommersperger, Shervin Dehghani, Mojtaba Esfandiari, Russel H Taylor, M Ali Nasseri, Peter Gehlbach, Nassir Navab, Iulian Iordachita

Abstract read
In one paragraph

Article in IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Toward comprehensive real-time scene understanding in ophthalmic surgery through multimodal image fusion.International journal of computer assisted radiology and surgery · 2026
    Article
  2. Article
  3. Article
  4. Surgical Approaches to Retinal Gene Therapy: 2025 Update.Bioengineering (Basel, Switzerland) · 2025
    Review
  5. A Deep Learning-Driven Autonomous System for Retinal Vein Cannulation: Validation Using a Chicken Embryo Model.Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems · 2025
    Article
  6. Deep Learning-Enhanced Robotic Subretinal Injection with Real-Time Retinal Motion Compensation.IEEE International Conference on Automation Science and Engineering (CASE) : [proceedings]. IEEE Conference on Automation Science and Engineering · 2025
    Article
  7. Model Predictive Path Integral Control of IProceedings of the ... American Control Conference. American Control Conference · 2025
    Article
  8. Article
  9. Towards Motion Compensation in Autonomous Robotic Subretinal Injections.... International Symposium on Medical Robotics. International Symposium on Medical Robotics · 2025
    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

10 authors.

Demir ArikanDepartment of Computer Science, Technische Universitat München, Munich 85748 Germany.
Peiyao ZhangLaboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA.
Michael SommerspergerDepartment of Computer Science, Technische Universitat München, Munich 85748 Germany.
Shervin DehghaniDepartment of Computer Science, Technische Universitat München, Munich 85748 Germany.
Mojtaba EsfandiariLaboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA.
Russel H TaylorLaboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA.
M Ali NasseriDepartment of Computer Science, Technische Universitat München, Munich 85748 Germany.
Peter GehlbachWilmer Eye Institute, Johns Hopkins Hospital, Baltimore, MD, USA.
Nassir Navabprofessor and head of the Chair for Computer Aided Medical Procedures & Augmented Reality, Technical University of Munich, 85748 Munich, Germany.
Iulian IordachitaLaboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, 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

Robotic platforms provide consistent and precise tool positioning that significantly enhances retinal microsurgery. Integrating such systems with intraoperative optical coherence tomography (iOCT) enables image-guided robotic interventions, allowing autonomous performance of advanced treatments, such as injecting therapeutic agents into the subretinal space. However, tissue deformations due to tool-tissue interactions constitute a significant challenge in autonomous iOCT-guided robotic subretinal injections. Such interactions impact correct needle positioning and procedure outcomes. This paper presents a novel method for autonomous subretinal injection under iOCT guidance that considers tissue deformations during the insertion procedure. The technique is achieved through real-time segmentation and 3D reconstruction of the surgical scene from densely sampled iOCT B-scans, which we refer to as B

Identifiers

PMID40937043
PMCPMC12422304

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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.