Radiologic aspects of COVID-19 pneumonia: Outcomes and thoracic complications

The lungs are COVID-19’s most important focus, as it induces inflammatory changes in the lungs that can lead to respiratory insufficiency. Reducing the supply of oxygen to human cells negatively impacts humans, and multiorgan failure with a high mortality rate may, in certain circumstances, occur. Radiological pulmonary evaluation is a vital part of patient therapy for the critically ill patient with COVID-19. The evaluation of radiological imagery is a specialized activity that requires a radiologist. Artificial intelligence to display radiological images is one of the essential topics. Using a deep machine learning technique to identify morphological differences in the lungs of COVID-19-infected patients could yield promising results on digital images of chest X-rays. Minor differences in digital images that are not detectable or apparent to the human eye may be detected using computer vision algorithms. This paper uses machine learning methods to diagnose COVID-19 on chest X-rays, and the findings have been very promising. The dataset includes COVID-19-enhanced X-ray images for disease detection using chest X-ray images. The data were gathered from two publicly accessible datasets. The feature extractions are done using the gray level co-occurrence matrix methods. K-nearest neighbor, support vector machine, linear discrimination analysis, naïve Bayes, and convolutional neural network methods are used for the classification of patients. According to the findings, convolutional neural networks’ efficiency linked to imaging modalities with fewer human involvements outperforms other traditional machine learning approaches.

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“…For analysis and simulation of the methods, 1824 image is used so that 80% belongs to training and 20% is the validation sample. [ 89 ].…”

Section: Resultsmentioning

confidence: 99%

An Analysis of New Feature Extraction Methods Based on Machine Learning Methods for Classification Radiological Images

Zadeh

Ardalani

Salehi

et al. 2022

Computational Intelligence and Neuroscience

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“…Sin embargo, de acuerdo con el tiempo de evolución de la enfermedad, los hallazgos pueden ser más específicos, encontrándose un patrón de vidrio despulido y consolidado con distribución periférica, de forma unilateral o bilateral en la primera etapa, los primeros cuatro días de la enfermedad; en los días 5 a 8 de evolución, un patrón de vidrio deslustrado y empedrado, con distribución periférica y bilateral; en la tercera etapa, de los días 10 a 13, las lesiones son bilaterales, ubicándose de forma periférica, con un patrón mixto compuesto por vidrio deslustrado y consolidación; y en la etapa 4 o de resolución, a partir del día 14, el patrón tomográfico característico se compone de consolidación acompañado de vidrio deslustrado, periférico y bilateral 32,33 . En nuestro estudio se reportó que los pacientes presentaron en promedio nueve días de evolución con síntomas característicos de COVID-19, sugiriendo que se encontraban en una etapa 2 o en fase de progresión, por lo cual el patrón tomográfico más común fue el de vidrio despulido y empedrado, sin embargo la distribución de lesiones fue periféricas y centrales.…”

Section: Discussionunclassified

Hallazgos por tomografía computada en pacientes de nuevo ingreso con diagnóstico de COVID-19. Estudio en un Instituto de la Seguridad Social en Tabasco

Sastré¹,

Azuara-Forcelledo²,

Ramírez-Chacón³

et al. 2023

ARM

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“…The pathophysiological mechanism of subcutaneous emphysema, pneumothorax, and spontaneous pneumomediastinum is unknown. However, regarding severe pneumonia associated with acute respiratory distress syndrome, possible etiologies have been described, such as diffuse alveolar damage, spontaneous rupture of a bulla, pneumatocele or subpleural cyst, and barotrauma caused by VILI (ventilation-induced lung injury) (9).…”

Section: Discussionmentioning

confidence: 99%

Spontaneous Pneumothorax and Pneumomediastinum in COVID 19: Case Series

Molano-Franco¹,

Albert²,

Victor³

et al. 2021

Int. J. Anesthesiol. Res.

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SARS COV2 infection can produce pneumothorax and spontaneous pneumomediastinum complications, which are associated with a worse prognosis. Here we present a series of cases of patients who presented subcutaneous emphysema caused by pneumothorax or spontaneous pneumomediastinum during care in the intensive care unit for pneumonia and COVID 19. This group of patients showed, in all cases, prolonged mechanical ventilation, refractory hypoxemia and hypercapnia, acute renal failure, bacterial superinfection, need for broad-spectrum antibiotics, and vasopressor support. Keywords: Subcutaneous Emphysema, Spontaneous Pneumothorax, Spontaneous Pneumomediastinum, COVID 19

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Radiologic aspects of COVID-19 pneumonia: Outcomes and thoracic complications

Cited by 17 publications

References 64 publications

An Analysis of New Feature Extraction Methods Based on Machine Learning Methods for Classification Radiological Images

An Analysis of New Feature Extraction Methods Based on Machine Learning Methods for Classification Radiological Images

Hallazgos por tomografía computada en pacientes de nuevo ingreso con diagnóstico de COVID-19. Estudio en un Instituto de la Seguridad Social en Tabasco

Spontaneous Pneumothorax and Pneumomediastinum in COVID 19: Case Series

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