ArticleJournal of pharmaceutical analysis2020
Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019.
Article in Journal of pharmaceutical analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers.
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.
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.
Who cites it
79 citing papers in PubMed.
- Nebulized inhalation of plasma-activated water in the treatment of progressive moderate COVID-19 patients with antiviral treatment failure: a randomized controlled pilot trial.BMC infectious diseases · 2024Trial
- Ferritin is associated with the severity of lung involvement but not with worse prognosis in patients with COVID-19: data from two Italian COVID-19 units.Scientific reports · 2021Trial
- AI-Based Quantitative HRCT for In-Hospital Adverse Outcomes and Exploratory Assessment of Reinfection in COVID-19.Diagnostics (Basel, Switzerland) · 2025Article
- Consolidation-to-ground-glass opacity ratio on chest CT as a prognostic marker for critical outcomes in COVID-19: a retrospective cohort study.BMC pulmonary medicine · 2025Article
- Lung Volume Change Assessment in Moderate and Severe COVID-19 Using CT Volumetry.Diagnostics (Basel, Switzerland) · 2025Article
- The relationship between different severity of COVID-19 pneumonia and arterial stiffness based on artificial intelligence analysis.Frontiers in medicine · 2025Article
- Age-related lung structure changes by quantitative assessment: a cross-sectional study in a Chinese male cohort.Frontiers in aging · 2025Article
- Multicenter study on CT-based Radiomics for predicting severity and delayed recovery inFrontiers in medicine · 2025Article
- Central artery pulse pressure, not central arterial stiffness impact on all-cause mortality in patients with viral pneumonia infection.BMC infectious diseases · 2024Article
- Predicting the severity of mycoplasma pneumoniae pneumonia in pediatric and adult patients: a multicenter study.Scientific reports · 2024Article
- Incorporation of Chest Computed Tomography Quantification to Predict Outcomes for Patients on Hemodialysis with COVID-19.Kidney diseases (Basel, Switzerland) · 2024Article
- Segmentation and classification of lungs CT-scan for detecting COVID-19 abnormalities by deep learning technique: U-Net model.Journal of family medicine and primary care · 2024Article
- Clinical utilization of artificial intelligence-based COVID-19 pneumonia quantification using chest computed tomography - a multicenter retrospective cohort study in Japan.Respiratory research · 2023Article
- Patients Hospitalized for COVID-19 in the Periods of Delta and Omicron Variant Dominance in Greece: Determinants of Severity and Mortality.Journal of clinical medicine · 2023Article
- Lung ultrasonography versus chest CT for assessing peripheric pulmonary lesions in hemorrhagic fever with renal syndrome: a prospective comparative study in China.European radiology · 2023Article
- Self-paced Multi-view Learning for CT-based severity assessment of COVID-19.Biomedical signal processing and control · 2023Article
- Prognosticating various acute covid lung disorders from COVID-19 patient using chest CT Images.Engineering applications of artificial intelligence · 2023Article
- Relation of impulse oscillometry and spirometry with quantitative thorax computed tomography after COVID-19 pneumonia.Revista da Associacao Medica Brasileira (1992) · 2023Article
- Efficient COVID-19 super pixel segmentation algorithm using MCFO-based SLIC.Journal of ambient intelligence and humanized computing · 2023Article
- Calibration and Validation of a Mechanistic COVID-19 Model for Translational Quantitative Systems Pharmacology - A Proof-of-Concept Model Development for Remdesivir.Clinical pharmacology and therapeutics · 2022Article
19 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To examine the feasibility of using a computer tool for stratifying the severity of Coronavirus Disease 2019 (COVID-19) based on computed tomography (CT) images. We retrospectively examined 44 confirmed COVID-19 cases. All cases were evaluated separately by radiologists (visually) and through an in-house computer software. The degree of lesions was visually scored by the radiologist, as follows, for each of the 5 lung lobes: 0, no lesion present; 1, <1/3 involvement; 2, >1/3 and < 2/3 involvement; and 3, >2/3 involvement. Lesion density was assessed based on the proportion of ground-glass opacity (GGO), consolidation and fibrosis of the lesions. The parameters obtained using the computer tool included lung volume (mL), lesion volume (mL), lesion percentage (%), and mean lesion density (HU) of the whole lung, right lung, left lung, and each lobe. The scores obtained by the radiologists and quantitative results generated by the computer software were tested for correlation. A Chi-square test was used to test the consistency of radiologist- and computer-derived lesion percentage in the right/left lung, upper/lower lobe, and each of the 5 lobes. The results showed a strong to moderate correlation between lesion percentage scores obtained by radiologists and the computer software (
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.