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Background: The COVID-19 pandemic, which affected the entire global population, had an impact on our health and quality of life. Many people had complications, were hospitalised or even died due to SARS-CoV-2 infection. The health systems of many countries had to radically change their way of functioning and scientists around the world worked intensively to develop a vaccine for the SARS-CoV-2 virus. Aim: The aim of this work is to assess the quality of life of patients who were hospitalised for COVID-19, using the SF-36 questionnaire. Methods: Between May and August 2022, we conducted a telephone assessment of quality of life in patients who were hospitalised for COVID-19 at the Temporary Hospital in Pyrzowice (Silesia, Poland), between November 2021 and January 2022. Results: Quality of life was significantly lower in women (p = 0.040), those with DM2 (p = 0.013), CKD (p = 0.041) and the vaccinated (p = 0.015). Conclusions: People with chronic kidney disease, diabetes mellitus and women had a lower quality of life after COVID-19 disease. However, people who were vaccinated for SARS-CoV-2 had a lower quality of life than non-vaccinated people did. This is possibly due to the higher mean age, and probably the higher disease burden, in the vaccinated group. [ABSTRACT FROM AUTHOR]

Diagnostic imaging has become an integral part of the healthcare system. In recent years, scientists around the world have been working on artificial intelligence-based tools that help in achieving better and faster diagnoses. Their accuracy is crucial for successful treatment, especially for imaging diagnostics. This study used a deep convolutional neural network to detect four categories of objects on digital chest X-ray images. The data were obtained from the publicly available National Institutes of Health (NIH) Chest X-ray (CXR) Dataset. In total, 112,120 CXRs from 30,805 patients were manually checked for foreign objects: vascular port, shoulder endoprosthesis, necklace, and implantable cardioverter-defibrillator (ICD). Then, they were annotated with the use of a computer program, and the necessary image preprocessing was performed, such as resizing, normalization, and cropping. The object detection model was trained using the You Only Look Once v8 architecture and the Ultralytics framework. The results showed not only that the obtained average precision of foreign object detection on the CXR was 0.815 but also that the model can be useful in detecting foreign objects on the CXR images. Models of this type may be used as a tool for specialists, in particular, with the growing popularity of radiology comes an increasing workload. We are optimistic that it could accelerate and facilitate the work to provide a faster diagnosis. [ABSTRACT FROM AUTHOR]