Evidence map›Paper›PMID 39816708›Full record

ArticleInternational journal of telemedicine and applications2025

Classification of Severity of Lung Parenchyma Using Saliency and Discrete Cosine Transform Energy in Computed Tomography of Patients With COVID-19.

Santiago Tello-Mijares, Francisco Flores, Fomuy Woo

Abstract read
In one paragraph

Article in International journal of telemedicine and applications, 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

3 authors.

Santiago Tello-MijaresBasic Sciences Department, Instituto Tecnológico de la Laguna Tecnológico Nacional de México Campus Laguna, Torreón, Coahuila, Mexico.ORCID https://orcid.org/0000-0002-2575-8251
Francisco FloresPostgraduate Department, Instituto Tecnológico de la Laguna Tecnológico Nacional de México Campus Laguna, Torreón, Coahuila, Mexico.ORCID https://orcid.org/0000-0002-6661-8191
Fomuy WooMedical Familiar Unit, Instituto de Seguridad y Servicios Sociales de Los Trabajadores del Estado, Torreón, Coahuila, Mexico.ORCID https://orcid.org/0000-0002-6586-7161

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study proposes an automated system for assessing lung damage severity in coronavirus disease 2019 (COVID-19) patients using computed tomography (CT) images. These preprocessed CT images identify the extent of pulmonary parenchyma (PP) and ground-glass opacity and pulmonary infiltrates (GGO-PIs). Two types of images-saliency (

Indexed as

artificial intelligencecomputed tomographyCOVID-19medical diagnostic imagingSARS-CoV-2 virus

Identifiers

PMID39816708
PMCPMC11729514

What OpenQuestion holds

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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.