ArticleEuropean radiology experimental2023
A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia.
Article in European radiology experimental, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 13 citations in OpenAlex.
- Long-term chest CT abnormalities up to 4.5 years after COVID-19 hospitalization and their association with dyspnea and fatigue: a prospective cohort study.Respiratory research · 2026Article
- CompositIA: an open-source automated quantification tool for body composition scores from thoraco-abdominal CT scans.European radiology experimental · 2025Article
- Severity assessment of COVID-19 disease: radiological visual score versus automated quantitative CT parameters using a pneumonia analysis algorithm.Frontiers in medicine · 2025Article
- Quantitative Chest CT Analysis: Three Different Approaches to Quantify the Burden of Viral Interstitial Pneumonia Using COVID-19 as a Paradigm.Journal of clinical medicine · 2024Article
- Quantitative CT Texture Analysis of COVID-19 Hospitalized Patients during 3-24-Month Follow-Up and Correlation with Functional Parameters.Diagnostics (Basel, Switzerland) · 2024Article
- Doubts and concerns about COVID-19 uncertainties on imaging data, clinical score, and outcomes.BMC pulmonary medicine · 2023Article
- Radiomics Applications in Spleen Imaging: A Systematic Review and Methodological Quality Assessment.Diagnostics (Basel, Switzerland) · 2023Review
- Accuracy of artificial intelligence CT quantification in predicting COVID-19 subjects' prognosis.PloS one · 2023Article
- Quantification of pulmonary involvement in COVID-19 pneumonia: an upgrade of theEuropean physical journal plus · 2023Article
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35 authors at 9 institutions in 1 country.
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Abstract
backgroundThe role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model.
methodsLungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model.
resultsDespite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81.
conclusionsVisual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts. KEY POINTS: We conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia.
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