Evidence map›Paper›PMID 34581706›Full record

ArticleJournal of computer assisted tomography

AI-Based Quantitative CT Analysis of Temporal Changes According to Disease Severity in COVID-19 Pneumonia.

Selin Ardali Duzgun, Gamze Durhan, Figen Basaran Demirkazik, Ilim Irmak, Jale Karakaya, Erhan Akpinar, Meltem Gulsun Akpinar, Ahmet Cagkan Inkaya, Serpil Ocal, Arzu Topeli and 1 more

Open access · greenAbstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of computer assisted tomography. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.2field-weighted citation impact, top 20% 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

5 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Review
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  4. Observational
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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

11 authors at 1 institution in 1 country.

Selin Ardali DuzgunFrom the Department of Radiology.
Gamze DurhanFrom the Department of Radiology.
Figen Basaran DemirkazikFrom the Department of Radiology.
Ilim IrmakFrom the Department of Radiology.
Jale KarakayaFrom the Department of Radiology.
Erhan AkpinarFrom the Department of Radiology.
Meltem Gulsun AkpinarFrom the Department of Radiology.
Ahmet Cagkan InkayaDepartment of Infectious Diseases and Clinical Microbiology.
Serpil OcalDepartment of Internal Medicine, Hacettepe University School of Medicine, Ankara, Turkey.
Arzu TopeliDepartment of Internal Medicine, Hacettepe University School of Medicine, Ankara, Turkey.
Orhan Macit AriyurekFrom the Department of Radiology.
Hacettepe University · TR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo quantitatively evaluate computed tomography (CT) parameters of coronavirus disease 2019 (COVID-19) pneumonia an artificial intelligence (AI)-based software in different clinical severity groups during the disease course.

methodsFrom March 11 to April 15, 2020, 51 patients (age, 18-84 years; 28 men) diagnosed and hospitalized with COVID-19 pneumonia with a total of 116 CT scans were enrolled in the study. Patients were divided into mild (n = 12), moderate (n = 31), and severe (n = 8) groups based on clinical severity. An AI-based quantitative CT analysis, including lung volume, opacity score, opacity volume, percentage of opacity, and mean lung density, was performed in initial and follow-up CTs obtained at different time points. Receiver operating characteristic analysis was performed to find the diagnostic ability of quantitative CT parameters for discriminating severe from nonsevere pneumonia.

resultsIn baseline assessment, the severe group had significantly higher opacity score, opacity volume, higher percentage of opacity, and higher mean lung density than the moderate group (all P ≤ 0.001). Through consecutive time points, the severe group had a significant decrease in lung volume (P = 0.006), a significant increase in total opacity score (P = 0.003), and percentage of opacity (P = 0.007). A significant increase in total opacity score was also observed for the mild group (P = 0.011). Residual opacities were observed in all groups. The involvement of more than 4 lobes (sensitivity, 100%; specificity, 65.26%), total opacity score greater than 4 (sensitivity, 100%; specificity, 64.21), total opacity volume greater than 337.4 mL (sensitivity, 80.95%; specificity, 84.21%), percentage of opacity greater than 11% (sensitivity, 80.95%; specificity, 88.42%), total high opacity volume greater than 10.5 mL (sensitivity, 95.24%; specificity, 66.32%), percentage of high opacity greater than 0.8% (sensitivity, 85.71%; specificity, 80.00%) and mean lung density HU greater than -705 HU (sensitivity, 57.14%; specificity, 90.53%) were related to severe pneumonia.

conclusionsAn AI-based quantitative CT analysis is an objective tool in demonstrating disease severity and can also assist the clinician in follow-up by providing information about the disease course and prognosis according to different clinical severity groups.

Indexed as

Artificial IntelligenceAdolescentAdultAgedAged, 80 and overCOVID-19Evaluation Studies as TopicFemaleHumansLungMaleMiddle AgedRadiographic Image Interpretation, Computer-AssistedReproducibility of ResultsRetrospective StudiesSARS-CoV-2

Identifiers

PMID34581706
PMCPMC8607923
OpenAlexW3202133856

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.