Radiology of viral pneumonia

Сперанская, А. А.; Novikova, Lubov; Baranova, Olga; Васильева, М. А.

doi:10.20862/0042-4676-2016-97-3-149-156

Cited by 7 publications

(3 citation statements)

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“…Analysis of images of radiation diagnostics. Radiation diagnostics is the science of using radiation to study the structure and function of normal and pathologically altered human organs and systems to prevent and recognize diseases [13], [14]. One of the main directions of state policy in the field of healthcare is improving the quality of medical care.…”

Section: Methodsmentioning

confidence: 99%

Detection of chest pathologies using autocorrelation functions

Abdikerimova

Shekerbek

Tulenbayev

et al. 2023

IJECE

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<p><span lang="EN-US">An important feature of image analysis is texture, seen in all images, from aerial and satellite images to microscopic images in biomedical research. A chest X-ray is the most common and effective method for diagnosing severe lung diseases such as cancer, pneumonia, and tuberculosis. The lungs are the largest X-ray object. The correct separation of the shapes and sizes of the contours of the lungs is an important reason for diagnosis, because of which an intelligent information environment can be created. Despite the use of X-rays, to identify the diagnosis, there is a chance that the disease will not be detected. In this sense, there is a risk of development, which may be fatal. The article deals with the problems of pneumonia clustering using the autocorrelation function to obtain the most accurate result. This provides a reliable tool for diagnosing lung radiographs. Image pre-processing and data shaping play an important role in revealing a well-functioning basis of the nervous system. Therefore, images from two classes were selected for the task: healthy and with pneumonia. This paper demonstrates the applicability of the autocorrelation function for highlighting interest in lung radiographs based on the fineness of textural features and k-means extraction.</span></p>

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Section: Methodsmentioning

confidence: 99%

Detection of chest pathologies using autocorrelation functions

Abdikerimova

Shekerbek

Tulenbayev

et al. 2023

IJECE

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“…3 а В общей выборке больных изучалось влияние следующих факторов на интервал «дебют заболевания -выявление КТ-4»: пол, возраст, наличие сопутствующих заболеваний. Было выявлены статистически значимое (р=0.033) увеличение сроков у мужчин: медиана составила 11 суток (7-16), у женщин 10 суток (6)(7)(8)(9)(10)(11)(12)(13)(14)(15). При исследовании зависимости интервала от коморбидности больных (за коморбидность принималось наличие 2-х и более значимых хронических заболеваний), достоверных различий не получено (р=0.076).…”

Section: таблица №1unclassified

“…При оценке КТ-картины в отсроченном периоде достоверных различий частоты встречаемости изменений между группами не получено, наблюдался практически полный регресс воспалительных изменений. При исследовании всей выборки было выявлено увеличение интервала «дебют заболевания -выявление КТ-4» (р=0.033) у мужчин ( 11 суток (7)(8)(9)(10)(11)(12)(13)(14)(15)(16))) по сравнению с женщинами 10 суток (6-15), у выписанных больных (12 суток (8-18)) по сравнению с умершими (10 суток (6-15)) (p<0.001). Выявлено значимое (р=0.003) сокращение сроков прогрессирования с увеличением возраста больных Заключение.…”

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Dynamics of Ct-Picture Changes in Patients With a Critical Degree of Lung Tissue Damage Caused by the Sars-Cov-2 Virus

Komissarova,

Godzenko,

Rumyantsev

et al. 2022

Rejr

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t was found that death from coronavirus infection is the more likely, the higher the degree of damage to the lung on the visual scale "CT 0-4". Thus, patients with pulmonary involvement of 75% or more are predictively poor in terms of outcome. Assessment of the influence of the rate of disease progression to CT-4 on mortality and the search for factors affecting this indicator may be of practical importance.The purpose of this study is comparison of the duration of progression of the CT picture to a critical degree in various "waves" of the pandemic, the search for factors influencing the interval of "debut diseases -CT 4", as well as the assessment of the state of the lung tissue after a total process in a long period of time.Materials and methods: a retrospective analysis of 708 patients (226 in the first wave, 482 in the second wave), hospitalized at City Clinical Hospital No. 15 and having a critical degree of lung. 25 discharged patients underwent a control CT scan 6 and 12 months after discharge from the hospital.Results: The study groups were comparable in terms of demographic characteristics and the degree of CT at admission in hospital. When comparing the interval "onset of the disease -detection of CT-4", no significant differences between the "waves" were found (p = 0.372). When assessing the CT-picture in the delayed period, there were no significant differences in the frequency of changes between the groups; an almost complete regression of inflammatory changes was observed. In the study of the entire sample, an increase in the interval "onset of the disease -detection of CT-4" (p = 0.033) was found in men (11 days (7-16)) compared with women for 10 days (6-15), and in discharged patients (12 days (8-18) compared with deaths (10 days (6-15)) (p <0.001). There was a significant reduction in the progression time with increasing age of patients (p = 0.003) Conclusions: Considering the data obtained on the possible influence of the timing of the progression of the CT picture on the mortality of patients, as well as the results of long-term observation, it is advisable to search for modifiable risk factors for the rapid progression of the disease.

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Yudin¹

2023

Metaphorical Signs in Computed Tomography of Chest and Abdomen

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Radiology of viral pneumonia

Cited by 7 publications

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Detection of chest pathologies using autocorrelation functions

Detection of chest pathologies using autocorrelation functions

Dynamics of Ct-Picture Changes in Patients With a Critical Degree of Lung Tissue Damage Caused by the Sars-Cov-2 Virus

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