ArticleJournal of healthcare engineering2021
Chest Computed Tomography Images in Neonatal Bronchial Pneumonia under the Adaptive Statistical Iterative Reconstruction Algorithm.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study was to explore the application value of chest computed tomography (CT) images processed by artificial intelligence (AI) algorithms in the diagnosis of neonatal bronchial pneumonia (NBP). The AI adaptive statistical iterative reconstruction (ASiR) algorithm was adopted to reconstruct the chest CT image to compare and analyze the effect of the reconstruction of CT image under the ASiR algorithm under different preweight and postweight values based on the objective measurement and subjective evaluation. 85 neonates with pneumonia treated in hospital from September 1, 2015, to July 1, 2020, were selected as the research objects to analyze their CT imaging characteristics. Subsequently, the peripheral blood of healthy neonates during the same period was collected, and the levels of C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) were detected. The efficiency of CT examination, CRP, ESR, and combined examination in the diagnosis of NBP was analyzed. The results showed that the subjective quality score, lung window subjective score, and mediastinal window subjective score were the highest after CT image reconstruction when the preweight value of the ASiR algorithm was 50%. After treatment, 79 NBP cases (92.9%) showed ground-glass features in CT images. Compared with the healthy neonates, the levels of CRP and ESR in the peripheral blood of neonates with bronchial pneumonia were much lower (
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