ArticleRespiratory research2026
Imaging biomarkers of post-COVID dyspnea: insights from machine learning CT patterns and parametric response mapping.
Article in Respiratory research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04406324 (COVID-19), which is not on this map. Cited by 1 paper.
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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.
COVID-19: Prospective Follow-up of Pulmonary Function, Sleep Disorders, Quality of Life and Post-traumatic Stress
Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDyspnea is one of the most common symptoms in the post-acute phase of COVID-19 pneumonia. Conventional pulmonary function tests and computed tomography (CT) scores often fail to show correlation with symptom severity, highlighting the need for more sensitive imaging biomarkers. Machine-learning–based quantitative CT analysis and parametric response mapping (PRM) can capture subtle structural and functional abnormalities that may be associated with persistent dyspnea.
methodsWe analyzed inspiratory and paired inspiratory–expiratory CT scans of early (3–6 months) post-COVID-19 pneumonia patients. Inspiratory CT images were segmented using a random forest algorithm to quantify lung parenchymal patterns. Paired inspiratory/expiratory scans were co-registered to derive ventilation metrics and PRM-defined functional small airway disease (fSAD), emphysema, emptying emphysema, and normal lung. Associations between imaging metrics and patient-reported dyspnea assessed by a visual analogue scale (VAS) were evaluated using univariable and multivariable linear regression, with adjustment for age, sex, BMI, and smoking history.
resultsOne hundred twenty-three patients had usable inspiratory CT scans, and 116 patients had paired inspiratory/expiratory scans of sufficient quality for analysis. In the adjusted multivariable models, greater PRM-defined functional small airway disease (fSAD) was positively associated with dyspnea (standardized β = 1.21, p = 0.002). Moreover, a lower standard deviation of dense ground-glass attenuation in the left lung (standardized β = −0.82, p = 0.033) and greater total volume of dense ground-glass opacities (standardized β = 0.71, p = 0.033) were independently associated with dyspnea.
conclusionsIn early post-COVID-19 pneumonia, machine-learning–based CT pattern recognition and PRM revealed that functional small airway disease, and the total volume and heterogeneity of lung dense ground-glass opacities are significantly associated with persistent dyspnea. These findings highlight the potential of quantitative CT to identify pulmonary imaging biomarkers relevant to long COVID symptom burden.
trial registrationClinicalTrials.gov (NCT04406324).
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