Evidence map›Paper›PMID 38254225›Full record

ArticleBMC research notes2024

HRCTCov19-a high-resolution chest CT scan image dataset for COVID-19 diagnosis and differentiation.

Iraj Abedi, Mahsa Vali, Bentolhoda Otroshi, Maryam Zamanian, Hamidreza Bolhasani

Open access · goldAbstract read
In one paragraph

Article in BMC research notes, 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
2.0field-weighted citation impact, top 15% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. 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

5 authors at 4 institutions in 1 country.

Iraj AbediDepartment of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Mahsa ValiDepartment of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.
Bentolhoda OtroshiDepartment of Radiology, School of Medicine, Arak University of Medical Sciences, Arak, Iran.
Maryam ZamanianDepartment of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Hamidreza BolhasaniDepartment of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. hamidreza.bolhasani@srbiau.ac.ir.
Isfahan University of Medical Sciences · IRArak University of Medical Sciences · IRIsfahan University of Technology · IRIslamic Azad University, Science and Research Branch · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionComputed tomography (CT) was a widely used diagnostic technique for COVID-19 during the pandemic. High-Resolution Computed Tomography (HRCT), is a type of computed tomography that enhances image resolution through the utilization of advanced methods. Due to privacy concerns, publicly available COVID-19 CT image datasets are incredibly tough to come by, leading to it being challenging to research and create AI-powered COVID-19 diagnostic algorithms based on CT images. DATA DESCRIPTION: To address this issue, we created HRCTCov19, a new COVID-19 high-resolution chest CT scan image collection that includes not only COVID-19 cases of Ground Glass Opacity (GGO), Crazy Paving, and Air Space Consolidation but also CT images of cases with negative COVID-19. The HRCTCov19 dataset, which includes slice-level and patient-level labeling, has the potential to assist in COVID-19 research, in particular for diagnosis and a distinction using AI algorithms, machine learning, and deep learning methods. This dataset, which can be accessed through the web at http://databiox.com , includes 181,106 chest HRCT images from 395 patients labeled as GGO, Crazy Paving, Air Space Consolidation, and Negative.

Indexed as

COVID-19AlgorithmsCOVID-19 TestingHumansThoraxTomography, X-Ray ComputedChest imageComputed tomographyCOVID-19CT scanDatasetMedical imaging

Identifiers

PMID38254225
PMCPMC10804784
OpenAlexW4391102163

What OpenQuestion holds

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LicenceCC BY
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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.