Evidence map›Paper›PMID 41767927›Full record

ArticleBiomedical optics express2025

SpecstatOR: speckle statistics-based iOCT segmentation network for ophthalmic surgery.

Kristina Mach, Hessam Roodaki, Michael Sommersperger, Nassir Navab

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical optics express, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Kristina MachChair for Computer Aided Medical Procedures and Augmented Reality, (I16), TUM School of Computation, Information and Technology, Technische Universitat Munchen, Boltzmannstr. 3, 85748 Garching, Germany.ORCID https://orcid.org/0000-0002-7492-3786
Hessam RoodakiAdvanced Development Department of Carl Zeiss Meditec AG, Kistlerhofstr. 75, 81379 Munich, Germany.
Michael SommerspergerChair for Computer Aided Medical Procedures and Augmented Reality, (I16), TUM School of Computation, Information and Technology, Technische Universitat Munchen, Boltzmannstr. 3, 85748 Garching, Germany.
Nassir NavabChair for Computer Aided Medical Procedures and Augmented Reality, (I16), TUM School of Computation, Information and Technology, Technische Universitat Munchen, Boltzmannstr. 3, 85748 Garching, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper introduces an approach to intraoperative optical coherence tomography (iOCT) segmentation, utilizing speckle patterns from tissue and tool scattering properties, defined by refractive index and structural composition, to differentiate retinal layers and instruments. Unlike classical deep learning approaches, our model trains on tissue-specific characteristics, enhancing robustness across different devices and anatomical variations and eliminating retraining. Consequently, our approach reduces the dependency on shape and intensity, addressing the limitations of state-of-the-art iOCT segmentation techniques used during surgical procedures.

Identifiers

PMID41767927
PMCPMC12945488

What Socratic 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 Socratic graph.