Thoracic Computed Tomography Imaging in Dengue Fever: A Tertiary Experience in South Indian Population

Abstract

Introduction: Dengue fever is one of the most common acute vector-borne viral illnesses affecting mankind. Despite an abundance of case material worldwide , cross-sectional lungimaging data in patients with dengue is scarce. The aim of the study was to detect and evaluate the thoracic CT findings in dengue fever. Material and methods: Chest CT findings of 30 patients with dengue fever in a 6 month period from July to December 2017 were studied. Results: The commonest chest CT findings were pleural effusion (n=20), atelectasis (n=14), ground-glass opacification (n=6), patchy consolidation (n=4), air space nodules (n=3), interstitial thickening (n=3) and pericardial effusion (n=1). Conclusion: CT thorax is a good modality for evaluation of dengue patients with respiratory complaints. Frequently detected findings on chest CT included pleural effusion, atelectasis and ground-glass opacification.

Intelligent Computational Model for the Classification of Covid-19 with Chest Radiography Compared to other Respiratory Diseases

“…The literature indicates that ground-glass pulmonary opacity patterns, usually with bilateral and peripheral pulmonary distribution, are emerging as a hallmark of COVID-19 infection. This disease pattern, somewhat similar to that described in previous coronavirus outbreaks, such as SARS and MERS, also fits the model that radiologists recognize as the archetypal response to acute lung injury, usually initiated by an infectious or inflammatory condition[4,7]. Inflammation can cause ground-glass opacities in lung images, indicating consolidated dense lesions that can progressively evolve to a linear structure[8][9].Our research efforts have shown that models using artificial intelligence can determine parameters for different groups with similar symptoms and signs.…”

“…These diseases can be associated with intrinsic factors such as immunity, contact pattern, renewal, virulence rates and extrinsic factors, such as temperature, humidity, and precipitation. Among these diseases, the most common are tuberculosis, malaria, Streptococcus pneumonia, and dengue [2][3][4][5][6][7]. As these different pathologies can generate conflicting signals in diagnostic imaging, we investigated metrics that can indicate biomarkers capable of avoiding the false-positive diagnosis of COVID-19.…”

“…These diseases can be associated with intrinsic factors such as immunity, contact pattern, renewal, virulence rates and extrinsic factors, such as temperature, humidity, and precipitation. Among these diseases, the most common are tuberculosis, malaria, Streptococcus pneumonia, and dengue [2][3][4][5][6][7] However, the intelligent computational model can help identify complications in the screening systems and the monitoring of pulmonary problems since there is still no effective drug for the disease, and the vaccines are still in the process of validation. The data obtained with our model suggest that the Haralick method can determine the patterns of pulmonary imaging characteristics showing pleural effusion, ground-glass opacity, pulmonary edema, rounded morphological opacities, and bronchitis.…”