Evidence map›Paper›PMID 33686526›Full record

ArticleJournal of digital imaging2021

Developing a Training Web Application for Improving the COVID-19 Diagnostic Accuracy on Chest X-ray.

P Menéndez Fernández-Miranda, P Sanz Bellón, A Pérez Del Barrio, L Lloret Iglesias, P Solís García, F Aguilar-Gómez, D Rodríguez González, J A Vega

Open access · hybridAbstract read
In one paragraph

Article in Journal of digital imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.0field-weighted citation impact, top 26% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. The Imaging Informatics Response to a Pandemic.Journal of digital imaging · 2021
    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

8 authors at 3 institutions in 3 countries.

P Menéndez Fernández-MirandaDepartamento de Radiología, Hospital Universitario "Marqués de Valdecilla", Santander, Spain. pablomenendezfernandezmiranda@gmail.com.ORCID http://orcid.org/0000-0003-4742-8955
P Sanz BellónDepartamento de Radiología, Hospital Universitario "Marqués de Valdecilla", Santander, Spain.ORCID http://orcid.org/0000-0001-6237-0611
A Pérez Del BarrioDepartamento de Radiología, Hospital Universitario "Marqués de Valdecilla", Santander, Spain.ORCID http://orcid.org/0000-0003-1613-6159
L Lloret IglesiasGrupo de Computación Avanzada y e-Ciencia, Instituto de Física de Cantabria, (IFCA), Consejo Superior de Investigaciones Científicas (CSIC), Santander, Spain.ORCID http://orcid.org/0000-0002-0157-4765
P Solís García, Wroclaw, Poland.
F Aguilar-GómezGrupo de Computación Avanzada y e-Ciencia, Instituto de Física de Cantabria, (IFCA), Consejo Superior de Investigaciones Científicas (CSIC), Santander, Spain.
D Rodríguez GonzálezGrupo de Computación Avanzada y e-Ciencia, Instituto de Física de Cantabria, (IFCA), Consejo Superior de Investigaciones Científicas (CSIC), Santander, Spain.ORCID http://orcid.org/0000-0002-9160-5106
J A VegaDepartamento de Morfología y Biología Celular, Universidad de Oviedo, Oviedo, Spain. javega@uniovi.es.ORCID http://orcid.org/0000-0003-1276-0018
Universidad de Oviedo · ESConsejo Superior de Investigaciones Científicas · ESUniversity of Wrocław · PL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In December 2019, a new coronavirus known as 2019-nCoV emerged in Wuhan, China. The virus has spread globally and the infection was declared pandemic in March 2020. Although most cases of coronavirus disease 2019 (COVID-19) are mild, some of them rapidly develop acute respiratory distress syndrome. In the clinical management, chest X-rays (CXR) are essential, but the evaluation of COVID-19 CXR could be a challenge. In this context, we developed COVID-19 TRAINING, a free Web application for training on the evaluation of COVID-19 CXR. The application included 196 CXR belonging to three categories: non-pathological, pathological compatible with COVID-19, and pathological non-compatible with COVID-19. On the training screen, images were shown to the users and they chose a diagnosis among those three possibilities. At any time, users could finish the training session and be evaluated through the estimation of their diagnostic accuracy values: sensitivity, specificity, predictive values, and global accuracy. Images were hand-labeled by four thoracic radiologists. Average values for sensitivity, specificity, and global accuracy were .72, .64, and .68. Users who achieved better sensitivity registered less specificity (p < .0001) and those with higher specificity decreased their sensitivity (p < .0001). Users who sent more answers achieved better accuracy (p = .0002). The application COVID-19 TRAINING provides a revolutionary tool to learn the necessary skills to evaluate COVID-19 on CXR. Diagnosis training applications could provide a new original manner of evaluation for medical professionals based on their diagnostic accuracy values, and an efficient method to collect valuable data for research purposes.

Indexed as

COVID-19Radiography, ThoracicHumansSARS-CoV-2Tomography, X-Ray ComputedX-RaysChest X-rayCOVID‐19Diagnostic accuracy valuesMedical applicationMedical educationTraining on diagnosis

Identifiers

PMID33686526
PMCPMC7939450
OpenAlexW3134817088

What OpenQuestion holds

Textmetadata
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.