Observational studyAnaesthesia, critical care & pain medicine2021
A novel, automated, quantification of abnormal lung parenchyma in patients with COVID-19 infection: Initial description of feasibility and association with clinical outcome.
Observational study in Anaesthesia, critical care & pain medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Estimating Postoperative Lung Function Using Three-Dimensional Segmental HRCT-Reconstruction: A Retrospective Pilot Study on Right Upper Lobe Resections.Journal of personalized medicine · 2025Article
- Observational
- Article
- Quantitative Chest CT Analysis to Measure Short-Term Sequelae of COVID-19 Pneumonia: A Monocentric Prospective Study.Tomography (Ann Arbor, Mich.) · 2022Article
- Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence.Emergency radiology · 2022Review
- Associations between CT pulmonary opacity score on admission and clinical characteristics and outcomes in patients with COVID-19.Internal and emergency medicine · 2022Article
- The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review.Diagnostics (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveGround-glass opacities are the most frequent radiologic features of COVID-19 patients. We aimed to determine the feasibility of automated lung volume measurements, including ground-glass volumes, on the CT of suspected COVID-19 patients. Our goal was to create an automated and quantitative measure of ground-glass opacities from lung CT images that could be used clinically for diagnosis, triage and research.
designSingle centre, retrospective, observational study. MEASUREMENTS: Demographic data, respiratory support treatment (synthetised in the maximal respiratory severity score) and CT-images were collected. Volume of abnormal lung parenchyma was measured with conventional semi-automatic software and with a novel automated algorithm based on voxels X-Ray attenuation. We looked for the relationship between the automated and semi-automated evaluations. The association between the ground-glass opacities volume and the maximal respiratory severity score was assessed. MAIN
resultsThirty-seven patients were included in the main outcome analysis. The mean duration of automated and semi-automated volume measurement process were 15 (2) and 93 (41) min, respectively (p=8.05*10
conclusionThe feasibility and preliminary validity of automated impaired lung volume measurements in a high-density COVID-19 cluster was confirmed by our results.
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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.