COVID-19 Disease and Vaccination: Knowledge, Fears, Perceptions and Feelings of Regret for Not Having Been Vaccinated among Hospitalized Greek Patients Suffering SARS-CoV-2 Infection.

BACKGROUND: The development of vaccines against COVID-19 has greatly altered the natural course of this infection, reducing the disease's severity and patients' hospitalization. However, hesitancy against vaccination remains an obstacle in the attempt to achieve appropriate herd immunity that could reduce the spread of SARS-CoV-2. The aim of this study was to investigate the perceptions and attitudes of COVID-19 patients hospitalized during the fourth pandemic wave in two Greek hospitals and assess whether their experience had changed their intentions regarding vaccination against COVID-19. METHODS: This is a cross-sectional, questionnaire-based survey, conducted from 31 August 2021 to 18 February 2022 in the COVID-19 departments of two tertiary care hospitals. The questionnaire included questions regarding the patients' educational level, knowledge and beliefs regarding SARS-CoV-2, personal protection measures, beliefs regarding vaccination, vaccination status, reasons for not been vaccinated against SARS-CoV-2, feelings of regret for not been vaccinated, and willingness to be vaccinated in the future. All adult patients with COVID-19 were eligible, regardless of their vaccination status against SARS-CoV-2. RESULTS: In total, 162 patients agreed and participated in the study, with 97% of them suffering severe COVID-19. Their median age was 56 years, and 59.9% (97 patients) were male. Among them, 43.8% had been vaccinated against COVID-19. When unvaccinated patients were asked the reasons for not being vaccinated, the most frequent responses were that they were waiting for more scientific data, due to uncertainty about long-term consequences of the vaccine, and their fear of thrombosis. When at discharge, unvaccinated hospitalized COVID-19 patients were asked whether they would get vaccinated if they could turn time back, and 64.7% of them replied positively. CONCLUSIONS: The study reveals several patients' fears and misconceptions and suggests that there is room for implementing measures that could reduce knowledge gaps allowing for improvement of vaccination rates against COVID-19.

Advancing COVID-19 differentiation with a robust preprocessing and integration of multi-institutional open-repository computer tomography datasets for deep learning analysis.

The coronavirus pandemic and its unprecedented consequences globally has spurred the interest of the artificial intelligence research community. A plethora of published studies have investigated the role of imaging such as chest X-rays and computer tomography in coronavirus disease 2019 (COVID-19) automated diagnosis. Οpen repositories of medical imaging data can play a significant role by promoting cooperation among institutes in a world-wide scale. However, they may induce limitations related to variable data quality and intrinsic differences due to the wide variety of scanner vendors and imaging parameters. In this study, a state-of-the-art custom U-Net model is presented with a dice similarity coefficient performance of 99.6% along with a transfer learning VGG-19 based model for COVID-19 versus pneumonia differentiation exhibiting an area under curve of 96.1%. The above was significantly improved over the baseline model trained with no segmentation in selected tomographic slices of the same dataset. The presented study highlights the importance of a robust preprocessing protocol for image analysis within a heterogeneous imaging dataset and assesses the potential diagnostic value of the presented COVID-19 model by comparing its performance to the state of the art.

Interpretable artificial intelligence framework for COVID-19 screening on chest X-rays.

COVID-19 has led to an unprecedented healthcare crisis with millions of infected people across the globe often pushing infrastructures, healthcare workers and entire economies beyond their limits. The scarcity of testing kits, even in developed countries, has led to extensive research efforts towards alternative solutions with high sensitivity. Chest radiological imaging paired with artificial intelligence (AI) can offer significant advantages in diagnosis of novel coronavirus infected patients. To this end, transfer learning techniques are used for overcoming the limitations emanating from the lack of relevant big datasets, enabling specialized models to converge on limited data, as in the case of X-rays of COVID-19 patients. In this study, we present an interpretable AI framework assessed by expert radiologists on the basis on how well the attention maps focus on the diagnostically-relevant image regions. The proposed transfer learning methodology achieves an overall area under the curve of 1 for a binary classification problem across a 5-fold training/testing dataset.