Evidence map›Paper›PMID 31547127›Full record

ArticleJournal of clinical medicine2019

A Novel Automatic Method to Estimate Visual Acuity and Analyze the Retinal Vasculature in Retinal Vein Occlusion Using Swept Source Optical Coherence Tomography Angiography.

Marta Díez-Sotelo, Macarena Díaz, Maximino Abraldes, Francisco Gómez-Ulla, Manuel G Penedo, Marcos Ortega

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Observational
  8. 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

6 authors.

Marta Díez-SoteloDepartment of Ophthalmology, Complejo Hospitalario Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain. martads_2@hotmail.com.ORCID 0000-0003-4247-0417
Macarena DíazDepartment of Computer Science, University of A Coruña, 15071 A Coruña, Spain. macarena.diaz1@udc.es.ORCID 0000-0002-6904-2968
Maximino AbraldesDepartment of Ophthalmology, Complejo Hospitalario Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain. maximinojose.abraldes@usc.es.
Francisco Gómez-UllaDepartment of Surgery, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain. franciscogomez-ulla@institutogomez-ulla.es.
Manuel G PenedoDepartment of Computer Science, University of A Coruña, 15071 A Coruña, Spain. mgpenedo@udc.es.
Marcos OrtegaDepartment of Computer Science, University of A Coruña, 15071 A Coruña, Spain. mortega@udc.es.

Funding

Fundación Mutua Madrileña 2017/365
6 · The paper itself

Abstract

The assessment of vascular biomarkers and their correlation with visual acuity is one of the most important issues in the diagnosis and follow-up of retinal vein occlusions (RVOs). The high workloads of clinical practice make it necessary to have a fast, objective, and automatic method to analyze image features and correlate them with visual function. The aim of this study is to propose a fully automatic system which is capable of estimating visual acuity (VA) in RVO eyes, based only on information obtained from macular optical coherence tomography angiography (OCTA) images. We also propose an automatic methodology to rapidly measure the foveal avascular zone (FAZ) area and the vascular density (VD) in the superficial and deep capillary plexuses in swept-source OCTA images centered on the fovea. The proposed methodology is validated using a representative sample of 133 visits of 50 RVO patients. Our methodology estimates VA with very high precision and is even more accurate when we integrate depth information, providing a high correlation index of 0.869 with the real VA, which outperforms the correlation index of 0.855 obtained when estimating VA from the data obtained by the semiautomatic existing method. In conclusion, the proposed method is the first computational system able to estimate VA in RVO, with the additional benefits of being automatic, less time-consuming, objective and more accurate. Furthermore, the proposed method is able to integrate depth information, a feature which is lacking in the existing method.

Indexed as

automatic methodimaging retinaoptical coherence tomography angiographyretinal vein occlusionvisual acuity estimation

Identifiers

PMID31547127
PMCPMC6832977

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.