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Quantifications of COVID-19 radiological pulmonary structures (Coronavirus infection)


In December 2019, a series of cases in China of the so-called COVID2019 were identified, which spread throughout the world, currently reaching more than 2,000,000 cases with 140,000 deaths. Currently, viral nucleic acid analysis based on RT-PCR (real-time Polymerase Chain Reaction) is used as the standard reference method for confirming COVID-19 infection. One of the main consequences is a pulmonary inflammatory process, which leads to a large part of deaths and hospitalizations. In this sense, computed tomography (CT) is the method of choice for these analyzes and has been considered the main imaging method with high sensitivity for detecting the consequences of COVID19 and even considered as a diagnostic method. COVID19 causes several lung damage, mainly inflammatory processes that are manifested with ground-glass radiological findings, among others. The evaluation is subjective and requires trained specialists. Improvements in visualization and quantification of structures and radiological findings are essential for better diagnosis and quantitative determination of the affected pulmonary percentage.In this proposal, using image processing, to generate algorithms to enhance the affected lung characteristics and to quantify these structures in relation to the lung volume. Methods of filtering, segmentation and extraction of objective characteristics in CT images will be used in the initial stages, during and after negative by RT-PCR, aiming at a temporal and spatial characterization, in front of the different pulmonary areas. This type of objective analysis will provide greater support for the diagnostic decisions of radiologists and clinicians, providing data support for the treatment, sequencing of cured patients, and generating technological processes to aid diagnosis and support classificatory data involving pulmonary structures affected by COVID19. (AU)

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(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
FATTORI ALVES, ALLAN FELIPE; ARRUDA MIRANDA, JOSE RICARDO; REIS, FABIANO; OLIVEIRA, ABNER ALVES; SANTANA SOUZA, SERGIO AUGUSTO; CASTELO BRANCO FORTALEZA, CARLOS MAGNO; TANNI, SUZANA ERICO; SOUZA CASTRO, JOSE THIAGO; PINA, DIANA RODRIGUES. Automatic algorithm for quantifying lung involvement in patients with chronic obstructive pulmonary disease, infection with SARS-CoV-2, paracoccidioidomycosis and no lung disease patients. PLoS One, v. 16, n. 6 JUN 10 2021. Web of Science Citations: 0.

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