Evidence map›Paper›PMID 33680560›Full record

ArticleBiomedical optics express2021

Real-time tool to layer distance estimation for robotic subretinal injection using intraoperative 4D OCT.

Michael Sommersperger, Jakob Weiss, M Ali Nasseri, Peter Gehlbach, Iulian Iordachita, Nassir Navab

Open access · goldAbstract read
In one paragraph

Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 2 pooled it
3.3field-weighted citation impact, top 7% of its field
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

25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 46 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Towards Motion Compensation in Autonomous Robotic Subretinal Injections.... International Symposium on Medical Robotics. International Symposium on Medical Robotics · 2025
    Article
  9. Article
  10. Real-time Deformation-aware Control for Autonomous Robotic Subretinal Injection under iOCT Guidance.IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation · 2025
    Article
  11. Stereo reconstruction from microscopic images for computer-assisted ophthalmic surgery.International journal of computer assisted radiology and surgery · 2025
    Article
  12. Article
  13. Review
  14. Review
  15. Autonomous Needle Navigation in Subretinal Injections via iOCT.IEEE robotics and automation letters · 2024
    Article
  16. Review
  17. Article
  18. Article
  19. Article
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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

6 authors at 2 institutions in 2 countries.

Michael SommerspergerJohns Hopkins University, Baltimore, MD 21218, USA.
Jakob WeissTechnical University of Munich, Germany.
M Ali NasseriTechnical University of Munich, Germany.
Peter GehlbachJohns Hopkins University, Baltimore, MD 21218, USA.
Iulian IordachitaJohns Hopkins University, Baltimore, MD 21218, USA.
Nassir NavabJohns Hopkins University, Baltimore, MD 21218, USA.
Johns Hopkins University · USTechnical University of Munich · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The emergence of robotics could enable ophthalmic microsurgical procedures that were previously not feasible due to the precision limits of manual delivery, for example, targeted subretinal injection. Determining the distance between the needle tip, the internal limiting membrane (ILM), and the retinal pigment epithelium (RPE) both precisely and reproducibly is required for safe and successful robotic retinal interventions. Recent advances in intraoperative optical coherence tomography (iOCT) have opened the path for 4D image-guided surgery by providing near video-rate imaging with micron-level resolution to visualize retinal structures, surgical instruments, and tool-tissue interactions. In this work, we present a novel pipeline to precisely estimate the distance between the injection needle and the surface boundaries of two retinal layers, the ILM and the RPE, from iOCT volumes. To achieve high computational efficiency, we reduce the analysis to the relevant area around the needle tip. We employ a convolutional neural network (CNN) to segment the tool surface, as well as the retinal layer boundaries from selected iOCT B-scans within this tip area. This results in the generation and processing of 3D surface point clouds for the tool, ILM and RPE from the B-scan segmentation maps, which in turn allows the estimation of the minimum distance between the resulting tool and layer point clouds. The proposed method is evaluated on iOCT volumes from ex-vivo porcine eyes and achieves an average error of 9.24

Identifiers

PMID33680560
PMCPMC7901333
OpenAlexW3123375714

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.