Abstract:Pulmonary complications, including acute respiratory distress syndrome (ARDS), are well described in P. falciparum (PF) and to a lesser extent in other malaria species. In non-endemic areas, malaria diagnosis may be overlooked; if a thorough travel history is not obtained on all patients with acute febrile illness. Three patients with malaria associated respiratory distress were admitted to our intensive care unit. The diagnosis was delayed; however, all patients received artesunate and intensive therapy with … Show more
Lung X-ray images, if processed using statistical and computational methods, can distinguish pneumonia from COVID-19. The present work shows that it is possible to extract lung X-ray characteristics to improve the methods of examining and diagnosing patients with suspected COVID-19, distinguishing them from malaria, dengue, H1N1, tuberculosis, and Streptococcus pneumonia. More precisely, an intelligent computational model was developed to process lung X-ray images and classify whether the image is of a patient with COVID-19. The images were processed and extracted their characteristics. These characteristics were the input data for an unsupervised statistical learning method, PCA, and clustering, which identified specific attributes of X-ray images with Covid-19. The introduction of statistical models allowed a fast algorithm, which used the X-means clustering method associated with the Bayesian Information Criterion (CIB). The developed algorithm efficiently distinguished each pulmonary pathology from X-ray images. The method exhibited excellent sensitivity. The average recognition accuracy of COVID-19 was 0.93 ± 0.051.
Lung X-ray images, if processed using statistical and computational methods, can distinguish pneumonia from COVID-19. The present work shows that it is possible to extract lung X-ray characteristics to improve the methods of examining and diagnosing patients with suspected COVID-19, distinguishing them from malaria, dengue, H1N1, tuberculosis, and Streptococcus pneumonia. More precisely, an intelligent computational model was developed to process lung X-ray images and classify whether the image is of a patient with COVID-19. The images were processed and extracted their characteristics. These characteristics were the input data for an unsupervised statistical learning method, PCA, and clustering, which identified specific attributes of X-ray images with Covid-19. The introduction of statistical models allowed a fast algorithm, which used the X-means clustering method associated with the Bayesian Information Criterion (CIB). The developed algorithm efficiently distinguished each pulmonary pathology from X-ray images. The method exhibited excellent sensitivity. The average recognition accuracy of COVID-19 was 0.93 ± 0.051.
“…Patients with malaria have also been reported to develop non-cardiogenic pulmonary edema and acute respiratory distress syndrome (ARDS) due to increased capillary permeability or endothelial damage (Fig. 15 ) [ 65 , 66 ]. Fig.…”
Acute abdominal pain can be seen in cases with parasitic diseases delivered to emergency departments. The diagnosis of the parasitic disease can be delayed because of the similar clinical signs encountered in other frequently seen causes of acute abdomen. Nevertheless, the features detected in imaging scans can be helpful in the diagnosis. The present study aims to raise awareness about abdominal parasitosis in emergency conditions and also to underline the association between imaging findings and the life cycle of parasites with illustrative cases.
“…Severe cases of malaria are frequently caused by Plasmodium falciparum, although geographically dependent, and are associated with a plethora of complications, including acute respiratory distress syndrome (ARDS) [3]. Data on incidence, risk factors and outcome of ARDS in the context of malaria are largely unknown.…”
Background Acute respiratory distress syndrome (ARDS) is a severe complication of malaria that remains largely unstudied. We aim to describe the development of ARDS associated with severe P. falciparum malaria, its management and impact on clinical outcome. Methods Retrospective observational study of adult patients admitted with severe P. falciparum malaria in an Intensive Care Unit (ICU) of a tertiary care hospital from Portugal from 2008 to 2018. A multivariate logistic regression analysis was used to identify factors associated with the development of ARDS, defined according to Berlin Criteria. Prognosis was assessed by case-fatality ratio, nosocomial infection and length of stay. Results 98 patients were enrolled, of which 32 (33%) developed ARDS, a median of 2 days after starting antimalarial medication (IQR 0-4, range 0-6). Length of stay in ICU and in hospital were significantly longer in patients who developed ARDS: 13 days (IQR 10-18) vs 3 days (IQR 2-5) and 21 days (IQR 15-30.5) vs 7 days (IQR 6-10), respectively. Overall casefatality ratio in ICU was 4.1% and did not differ between groups. The risk of ARDS development is difficult to establish.
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