Evidence map›Paper›PMID 32607125›Full record

ArticleNature photonics2020

Transscleral Optical Phase Imaging of the Human Retina.

T Laforest, M Künzi, L Kowalczuk, D Carpentras, F Behar-Cohen, C Moser

Open access · greenAbstract read
In one paragraph

Article in Nature photonics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
3.4field-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

38 citing papers in PubMed, 54 citations in OpenAlex.

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  19. Multispectral label-freeJournal of biomedical optics · 2024
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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 3 institutions in 2 countries.

T Laforest *Laboratory of Applied Photonic Devices (LAPD), School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
M Künzi *Laboratory of Applied Photonic Devices (LAPD), School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
L KowalczukFaculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
D CarpentrasLaboratory of Applied Photonic Devices (LAPD), School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
F Behar-CohenFaculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
C MoserLaboratory of Applied Photonic Devices (LAPD), School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
École Polytechnique Fédérale de Lausanne · CHFondation Asile des Aveugles · CHInserm · FR

Funding

Swiss National Science Foundation 178253
6 · The paper itself

Abstract

In-vivo observation of the human retina at the cellular level is crucial to detect the first signs of retinal diseases and properly treat them. Despite the phenomenal advances in adaptive optics (AO) systems, clinical imaging of many retinal cells is still elusive due to the low signal-to-noise ratio induced by transpupillary illumination. We present a transscleral optical phase imaging (TOPI) method, which relies on high-angle oblique illumination of the retina, combined with AO, to enhance cell contrast. Examination of eleven healthy volunteer eyes, without pupil dilation, shows the ability of this method to produce in-vivo images of retinal cells, from the retinal pigment epithelium to the nerve fibre layer. This method also allows the generation of high-resolution label-free ex-vivo phase images of flat-mounted retinas. The 4.4°x 4.4° field-of-view in-vivo images are recorded in less than 10 seconds, opening new avenues in the exploration of healthy and diseased retinas.

Identifiers

PMID32607125
PMCPMC7326609
OpenAlexW2944935056

What OpenQuestion holds

Textmetadata
LicenceTDM
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.