Covid-19 imaging: A narrative review

The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis

Artificial intelligence (AI) has been shown to solve several issues affecting COVID-19 diagnosis. This systematic review research explores the impact of AI in early COVID-19 screening, detection, and diagnosis. A comprehensive survey of AI in the COVID-19 literature, mainly in the context of screening and diagnosis, was observed by applying the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Data sources for the years 2020, 2021, and 2022 were retrieved from google scholar, web of science, Scopus, and PubMed, with target keywords relating to AI in COVID-19 screening and diagnosis. After a comprehensive review of these studies, the results found that AI contributed immensely to improving COVID-19 screening and diagnosis. Some proposed AI models were shown to have comparable (sometimes even better) clinical decision outcomes, compared to experienced radiologists in the screening/diagnosing of COVID-19. Additionally, AI has the capacity to reduce physician work burdens and fatigue and reduce the problems of several false positives, associated with the RT-PCR test (with lower sensitivity of 60-70%) and medical imaging analysis. Even though AI was found to be timesaving and cost-effective, with less clinical errors, it works optimally under the supervision of a physician or other specialists.

CovidConvLSTM: A fuzzy ensemble model for COVID-19 detection from chest X-rays

The rapid outbreak of COVID-19 has affected the lives and livelihoods of a large part of the society. Hence, to confine the rapid spread of this virus, early detection of COVID-19 is extremely important. One of the most common ways of detecting COVID-19 is by using chest X-ray images. In the literature, it is found that most of the research activities applied convolutional neural network (CNN) models where the features generated by the last convolutional layer were directly passed to the classification models. In this paper, convolutional long short-term memory (ConvLSTM) layer is used in order to encode the spatial dependency among the feature maps obtained from the last convolutional layer of the CNN and to improve the image representational capability of the model. Additionally, the squeeze-and-excitation (SE) block, a spatial attention mechanism, is used to allocate weights to important local features. These two mechanisms are employed on three popular CNN models - VGG19, InceptionV3, and MobileNet to improve their classification strength. Finally, the Sugeno fuzzy integral based ensemble method is used on these classifiers' outputs to enhance the detection accuracy further. For experiments, three chest X-ray datasets, which are very prevalent for COVID-19 detection, are considered. For all the three datasets, it is found that the results obtained by the proposed method are comparable to state-of-the-art methods. The code, along with the pre-trained models, can be found at https://github.com/colabpro123/CovidConvLSTM.

Systematic lung ultrasound in Omicron-type vs. wild-type COVID-19

AIMS

Preliminary data suggested that patients with Omicron-type-Coronavirus-disease-2019 (COVID-19) have less severe lung disease compared with the wild-type-variant. We aimed to compare lung ultrasound (LUS) parameters in Omicron vs. wild-type COVID-19 and evaluate their prognostic implications.

METHODS AND RESULTS

One hundred and sixty-two consecutive patients with Omicron-type-COVID-19 underwent LUS within 48 h of admission and were compared with propensity-matched wild-type patients (148 pairs). In the Omicron patients median, first and third quartiles of the LUS-score was 5 [2-12], and only 9% had normal LUS. The majority had either mild (≤5; 37%) or moderate (6-15; 39%), and 15% (≥15) had severe LUS-score. Thirty-six percent of patients had patchy pleural thickening (PPT). Factors associated with LUS-score in the Omicron patients included ischaemic-heart-disease, heart failure, renal-dysfunction, and C-reactive protein. Elevated left-filling pressure or right-sided pressures were associated with the LUS-score. Lung ultrasound-score was associated with mortality [odds ratio (OR): 1.09, 95% confidence interval (CI): 1.01-1.18; P = 0.03] and with the combined endpoint of mortality and respiratory failure (OR: 1.14, 95% CI: 1.07-1.22; P < 0.0001). Patients with the wild-type variant had worse LUS characteristics than the matched Omicron-type patients (PPT: 90 vs. 34%; P < 0.0001 and LUS-score: 8 [5, 12] vs. 5 [2, 10], P = 0.004), irrespective of disease severity. When matched only to the 31 non-vaccinated Omicron patients, these differences were attenuated.

CONCLUSION

Lung ultrasound-score is abnormal in the majority of hospitalized Omicron-type patients. Patchy pleural thickening is less common than in matched wild-type patients, but the difference is diminished in the non-vaccinated Omicron patients. Nevertheless, even in this milder form of the disease, the LUS-score is associated with poor in-hospital outcomes.

COVID-19 and Inflammatory Markers

Coronavirus disease-2019 (COVID-19) causes mild illness to serious infection with lung involvement, thrombosis, and other complications potentially resulting in fatal outcomes. Recognised inflammatory biomarkers play important roles in managing patients with COVID-19; for example, diagnosis, follow-up, assessment of treatment response, and risk stratification. Inflammatory markers in COVID-19 disease were analysed in two categories. Well-known inflammatory markers include complete blood count, C-reactive protein, albumin, cytokines, and erythrocyte sedimentation rate. Asymmetric dimethylarginine, endocan, pentraxin 3, serum amyloid A, soluble urokinase plasminogen activator receptor, total oxidant status and total antioxidant status, and galectin-3 are considered among the emerging inflammatory markers. This brief narrative review assesses the relationship between these inflammatory markers and COVID-19 infection.

COVID-19 in Children: Update on Diagnosis and Management

In December 2019, a new infectious disease called coronavirus disease 2019 (COVID-19) attributed to the new virus named severe scute respiratory syndrome coronavirus 2 (SARS-CoV-2) was detected. The gold standard for the diagnosis of SARS-CoV-2 infection is the viral identification in nasopharyngeal swab by real-time polymerase chain reaction. Few data on the role of imaging are available in the pediatric population. Similarly, considering that symptomatic therapy is adequate in most of the pediatric patients with COVID-19, few pediatric pharmacological studies are available. The main aim of this review is to describe and discuss the scientific literature on various imaging approaches and therapeutic management in children and adolescents affected by COVID-19. Clinical manifestations of COVID-19 are less severe in children than in adults and as a consequence the radiologic findings are less marked. If imaging is needed, chest radiography is the first imaging modality of choice in the presence of moderate-to-severe symptoms. Regarding therapy, acetaminophen or ibuprofen are appropriate for the vast majority of pediatric patients. Other drugs should be prescribed following an appropriate individualized approach. Due to the characteristics of COVID-19 in pediatric age, the importance of strengthening the network between hospital and territorial pediatrics for an appropriate diagnosis and therapeutic management represents a priority.