Pixel-based analysis of pulmonary changes on CT lung images due to COVID-19 pneumonia

Abstract

Objectives: Computed tomography (CT) plays a complementary role in the diagnosis of the pneumonia-burden of COVID-19 disease. However, the low contrast of areas of inflammation on CT images, areas of infection are difficult to identify. The purpose of this study is to develop a post-image-processing method for quantitative analysis of COVID-19 pneumonia-related changes in CT attenuation values using a pixel-based analysis rather than more commonly used clustered focal pneumonia volumes. The COVID-19 pneumonia burden is determined by experienced radiologists in the clinic. Previous AI software was developed for the measurement of COVID-19 lesions based on the extraction of local pneumonia features. In this respect, changes in the pixel levels beyond the clusters may be overlooked by deep learning algorithms. The proposed technique focuses on the quantitative measurement of COVID-19 related pneumonia over the entire lung in pixel-by-pixel fashion rather than only clustered focal pneumonia volumes. Material and Methods: Fifty COVID-19 and 50 age-matched negative control patients were analyzed using the proposed technique and commercially available artificial intelligence (AI) software. The %pneumonia was calculated using the relative volume of parenchymal pixels within an empirically defined CT density range, excluding pulmonary airways, vessels, and fissures. One-way ANOVA analysis was used to investigate the statistical difference between lobar and whole lung %pneumonia in the negative control and COVID-19 cohorts. Results: The threshold of high-and-low CT attenuation values related to pneumonia caused by COVID-19 were found to be between ₋642.4 HU and 143 HU. The %pneumonia of the whole lung, left upper, and lower lobes were 8.1 ± 4.4%, 6.1 ± 4.5, and 11.3 ± 7.3% for the COVID-19 cohort, respectively, and statistically different (P < 0.01). Additionally, the pixel-based methods correlate well with existing AI methods and are approximately four times more sensitive to pneumonia particularly at the upper lobes compared with commercial software in COVID-19 patients (P < 0.01). Conclusion: Pixel-by-pixel analysis can accurately assess pneumonia in COVID-19 patients with CT. Pixel-based techniques produce more sensitive results than AI techniques. Using the proposed novel technique, %pneumonia could be quantitatively calculated not only in the clusters but also in the whole lung with an improved sensitivity by a factor of four compared to AI-based analysis. More significantly, pixel-by-pixel analysis was more sensitive to the upper lobe pneumonia, while AI-based analysis overlooked the upper lung pneumonia region. In the future, this technique can be used to investigate the efficiency of vaccines and drugs and post COVID-19 effects.

Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study

“…Another important issue that is often omitted in epidemiological and ML studies of COVID-19 is the close correlation of many predictors to age and sex, two inherent features of every patient. The investigations comparing the age- and sex-matched patients using ML approaches are currently limited to the deep profiling of circulating proteins (including cytokines), lipids, metabolites, and miRNA collected before or around the time of hospital admission and predicting the COVID-19 positivity [ 28 , 29 , 30 , 31 ] or disease severity [ 30 , 31 , 32 , 33 , 34 , 35 ], too small single-center studies of coagulation/fibrinolysis markers in predicting mortality [ 36 ], and to the studies of the lung computed tomography images [ 37 ] or electrocardiograms [ 38 ] to diagnose [ 37 ] or exclude [ 38 ] COVID-19. The matching of study cohorts by age and sex reduces the background behind the risk factors and clarifies the pathophysiological links between the comorbidities, organ damage, and serum biomarkers.…”

Detection of lung disease using relative reconstruction method in electrical impedance tomography system

“…Some of the most common lung diseases are pneumonia, pulmonary tuberculosis, bronchitis, asthma, chronic obstructive pulmonary disease (COPD), and lung cancer. Recently, the pathogenesis of corona virus disease 2019 (COVID-19) is also associated with disease progression in the lungs [1]. Lung disease can be diagnosed by image-based medical devices, including radiography, computerized tomography (CT)-scan and magnetic resonance imagery (MRI).…”