Evidence map›Paper›PMID 39679394›Full record

ArticleBiomedical optics express2024

Identifying retinal pigment epithelium cells in adaptive optics-optical coherence tomography images with partial annotations and superhuman accuracy.

Somayyeh Soltanian-Zadeh, Katherine Kovalick, Samira Aghayee, Donald T Miller, Zhuolin Liu, Daniel X Hammer, Sina Farsiu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical optics express, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

7 authors.

Somayyeh Soltanian-ZadehDepartment of Biomedical Engineering, Duke University, Durham, NC 27708, USA.ORCID https://orcid.org/0000-0003-2726-8501
Katherine KovalickCenter for Devices and Radiological Health (CDRH), U.S. Food and Drug Administration, Silver Spring, MD 20993, USA.
Samira AghayeeCenter for Devices and Radiological Health (CDRH), U.S. Food and Drug Administration, Silver Spring, MD 20993, USA.
Donald T MillerSchool of Optometry, Indiana University, Bloomington, IN 47405, USA.
Zhuolin LiuCenter for Devices and Radiological Health (CDRH), U.S. Food and Drug Administration, Silver Spring, MD 20993, USA.ORCID https://orcid.org/0000-0001-8019-2054
Daniel X HammerCenter for Devices and Radiological Health (CDRH), U.S. Food and Drug Administration, Silver Spring, MD 20993, USA.ORCID https://orcid.org/0000-0003-0532-1914
Sina FarsiuDepartment of Biomedical Engineering, Duke University, Durham, NC 27708, USA.ORCID https://orcid.org/0000-0003-4872-2902

Funding

VISION RESEARCHP30EY005722 · NEI · DUKE UNIVERSITY · PI Vadim Y Arshavsky · 1985 to 2026
$19.3M
Optical Imaging of the Photoreceptor-RPE ComplexR01EY018339 · NEI · TRUSTEES OF INDIANA UNIVERSITY · PI MILLER, DONALD T · 2007 to 2025
$7.7M
Photoreceptor Disease in Inherited Retinal DegenerationsR01EY035675 · NEI · UNIVERSITY OF PENNSYLVANIA · PI ARTUR V CIDECIYAN · 2024 to 2026
$1.7M
NEI NIH HHS P30 EY005722NEI NIH HHS R01 EY018339NEI NIH HHS R01 EY035675
6 · The paper itself

Abstract

Retinal pigment epithelium (RPE) cells are essential for normal retinal function. Morphological defects in these cells are associated with a number of retinal neurodegenerative diseases. Owing to the cellular resolution and depth-sectioning capabilities, individual RPE cells can be visualized in vivo with adaptive optics-optical coherence tomography (AO-OCT). Rapid, cost-efficient, and objective quantification of the RPE mosaic's structural properties necessitates the development of an automated cell segmentation algorithm. This paper presents a deep learning-based method with partial annotation training for detecting RPE cells in AO-OCT images with accuracy better than human performance. We have made the code, imaging datasets, and the manual expert labels available online.

Identifiers

PMID39679394
PMCPMC11640571

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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.