Evidence map›Paper›PMID 39508792›Full record

ArticleRevista medica del Instituto Mexicano del Seguro Social2024

Macular edema in retinal fundus images by a computational algorithm

César Augusto Garrido-Pino, Luis Miguel López-Montero, Leonel López-Lozano, Martha Alicia Hernández-González, Iván Cruz-Aceves

Abstract read
In one paragraph

Article in Revista medica del Instituto Mexicano del Seguro Social, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. [Artificial intelligence algorithm for bone age estimation].Revista medica del Instituto Mexicano del Seguro Social · 2026
    Observational
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

5 authors.

César Augusto Garrido-PinoInstituto Mexicano del Seguro Social, Centro Médico Nacional del Bajío, Hospital de Especialidades No. 1, Departamento de Oftalmología. León, Guanajuato, México.ORCID 0009-0009-4899-0991
Luis Miguel López-MonteroInstituto Mexicano del Seguro Social, Centro Médico Nacional del Bajío, Hospital de Especialidades No. 1, Departamento de Oftalmología. León, Guanajuato, México.ORCID 0000-0003-2826-6022
Leonel López-LozanoInstituto Mexicano del Seguro Social, Unidad Médica de Atención Ambulatoria No. 55, Servicio de Oftalmología. León, Guanajuato, México.ORCID 0009-0009-0536-5328
Martha Alicia Hernández-GonzálezInstituto Mexicano del Seguro Social, Centro Médico Nacional del Bajío, Hospital de Especialidades No. 1, División de Investigación en Salud. León, Guanajuato, México.ORCID 0000-0002-6903-2233
Iván Cruz-AcevesConsejo Nacional de Ciencia y Tecnología, Centro de Investigación en Matemáticas. Guanajuato, Guanajuato, México.ORCID 0000-0002-5197-2059

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Diabetes is a metabolic disease highly prevalent in our country that ends in disabling complications such as diabetic retinopathy and macular edema. As a high-impact socioeconomic issue, it is important to look for an early diagnostic test that also allows us to introduce a telemedicine service for the population with little access to specialized health services. Objective: To describe the performance in discrimination of macular edema of a feature detection algorithm on retinal fundus images from diabetic patients. Material and methods: We use a database of 266 retinal fundus images of diabetic patients and were classified in Macular Edema or Without Macular Edema by ophthalmologists' assessment and we test if the algorithm was capable of establish the presence or not of macular edema. Results: We made 3 tests in which the standards of sensitivity, specificity and efficiency of the algorithm were increasing according to the amount of retinal fundus images in the training phase, reaching specificity values of 100%, sensitivity 84% and efficiency 91.30%. Conclusions: Our study lays the foundation of a reliable screening method due to its high specificity value and allows not only a binary reply in the presence or not of macular edema but the clinical and topographic classification favoring the onset of treatment.

Indexed as

AlgorithmsDiabetic RetinopathyFundus OculiMacular EdemaSensitivity and SpecificityAgedFemaleHumansMaleMiddle AgedArtificial IntelligenceDiabetic RetinopathyFundus OculiMacular EdemaScreening

Identifiers

PMID39508792
PMCPMC12129567

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Registered trials

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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.