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Chest computed tomography (CT) has played a valuable, distinct role in the screening, diagnosis, and follow-up of COVID-19 patients. The quantification of COVID-19 pneumonia on CT has proven to be an important predictor of the treatment course and outcome of the patient although it remains heavily reliant on the radiologist's subjective perceptions. Here, we show that with the adoption of CT for COVID-19 management, a new type of psychophysical bias has emerged in radiology. A preliminary survey of 40 radiologists and a retrospective analysis of CT data from 109 patients from two hospitals revealed that radiologists overestimated the percentage of lung involvement by 10.23 ± 4.65% and 15.8 ± 6.6%, respectively. In the subsequent randomised controlled trial, artificial intelligence (AI) decision support reduced the absolute overestimation error ( P  < 0.001) from 9.5% ± 6.6 (No-AI analysis arm, n = 38) to 1.0% ± 5.2 (AI analysis arm, n = 38). These results indicate a human perception bias in radiology that has clinically meaningful effects on the quantitative analysis of COVID-19 on CT. The objectivity of AI was shown to be a valuable complement in mitigating the radiologist’s subjectivity, reducing the overestimation tenfold. Trial registration: https://Clinicaltrial.gov . Identifier: NCT05282056, Date of registration: 01/02/2022.

Background Although the neurological sequelae of severe COVID-19 are increasingly recognized, the long-term neural impact of uncomplicated mild SARS-CoV-2 infection remains insufficiently characterized. This study investigated post-infection alterations in piriform cortex activity, a region central to olfactory processing and integrative cognitive-emotional networks, and their associations with cognitive outcomes in adults. Methods Fifty adults with pre- and post-SARS-CoV-2 magnetic resonance imaging (MRI) and clinical/neuropsychological data were analyzed. A demographically matched control group of 50 individuals without prior COVID-19 or vaccination was included. Resting-state functional MRI assessed amplitude of low-frequency fluctuations (ALFF) within the piriform cortex as an index of regional neural activity. Results Baseline piriform ALFF did not differ between the two groups ( p  = 0.421). Following infection, piriform ALFF exhibited a significant quadratic trajectory ( p  = 0.003), characterized by early elevation relative to baseline, a decline to a nadir at approximately three months, and subsequent recovery toward baseline levels by six months. Greater acute COVID-19 symptom burden correlated with higher piriform ALFF ( β  = 0.34, p  = 0.017). Elevated ALFF was associated with reduced processing speed ( β = -0.52, p  = 0.001). Mediation analysis demonstrated that piriform ALFF significantly mediated the relationship between symptom burden and processing speed (indirect effect b = -0.081, p  = 0.037). Conclusions Piriform cortex function demonstrated a dynamic, nonlinear pattern within six months after mild COVID-19, marked by early hyperactivity, later downregulation, and gradual normalization. Altered piriform activity mediated the association between symptom burden and processing speed deficits, suggesting transient post-infection neurophysiological disruption with potential relevance to cognitive symptoms. Longitudinal follow-up is warranted to clarify underlying mechanisms and long-term implications.

Background Risk stratification of COVID-19 patients can support therapeutic decisions, planning and resource allocation in the hospital. In times of high incidence, a prognostic model based on data efficiently retrieved from one source can enable fast decision support. Methods A model was developed to identify patients at risk of developing severe COVID-19 within one month based on their age, sex and imaging features extracted from the thoracic computed tomography (CT). The model was trained on publicly available data from the Study of Thoracic CT in COVID-19 (STOIC) challenge and validated on unseen data from the same study and an external, multicentric dataset. The model, trained on data acquired before any variant of concern dominated, was assessed separately on data collected at later stages of the pandemic when the delta and omicron variants were most prevalent. Results A logistic regression based on handcrafted features was found to perform on par with a direct deep learning approach, and the former was selected for simplicity. Volumetric and intensity-based features of lesions and healthy lung parenchyma proved most predictive, in addition to patient age and sex. The model reached an area under the curve of 0.78 on the challenge test set and 0.74 on the external test set. The performance did not drop for the subset acquired at a later stage of the pandemic. Conclusions A logistic regression utilizing features from thoracic CT and its metadata can provide rapid decision support by estimating short-term COVID-19 severity. Its stable performance underscores its potential for real-world clinical integration. By enabling rapid risk stratification using readily available imaging data, this approach can support early clinical decision-making, optimize resource allocation, and improve patient management, particularly during surges in COVID-19 cases. Furthermore, this study provides a foundation for future research on prognostic modelling in respiratory infections.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread to nearly every corner of the globe, causing societal instability. The resultant coronavirus disease 2019 (COVID-19) leads to fever, sore throat, cough, chest and muscle pain, dyspnoea, confusion, anosmia, ageusia and headache. These can progress to life-threatening respiratory insufficiency, also affecting the heart, kidney, liver and nervous systems. The diagnosis of SARS-CoV-2 infection is often confused with that of influenza and seasonal upper respiratory tract viral infections. Due to available treatment strategies and required containments, rapid diagnosis is mandated. This Review brings clarity to the rapidly growing body of available and in-development diagnostic tests, including nanomaterial-based tools. It serves as a resource guide for scientists, physicians, students and the public at large. This Review highlights the progress that has been made in the development of diagnostic tools for the detection of SARS-CoV-2 in the fight against COVID-19.

COVID-19 has tremendously impacted patients and medical systems globally. Computed tomography images can effectively complement the reverse transcription-polymerase chain reaction testing. This study adopted a convolutional neural network for COVID-19 testing. We examined the performance of different pre-trained models on CT testing and identified that larger, out-of-field datasets boost the testing power of the models. This suggests that a priori knowledge of the models from out-of-field training is also applicable to CT images. The proposed transfer learning approach proves to be more successful than the current approaches described in literature. We believe that our approach has achieved the state-of-the-art performance in identification thus far. Based on experiments with randomly sampled training datasets, the results reveal a satisfactory performance by our model. We investigated the relevant visual characteristics of the CT images used by the model; these may assist clinical doctors in manual screening.

Dexamethasone was shown to decrease the mortality in coronavirus disease-2019 (COVID-19) recently. Use of corticosteroids was harmful in other coronavirus infections previously. WHO recommended against routine use of corticosteroids in COVID-19. In view of these, we reviewed the evidence about the use of corticosteroids in virus-induced acute respiratory distress syndrome (ARDS). Corticosteroids are beneficial in ARDS regardless of etiology. However, they increased the mortality rate in influenza-associated ARDS. In SARS and the Middle East respiratory syndrome, corticosteroids increased the mortality, delayed the viral clearance and increased the length of hospital stay. In the case of COVID-19, the available evidence from retrospective and observational studies is inconclusive about the corticosteroid use. Low-dose therapies appear to be effective. Evidence from a randomized control study found dexamethasone is effective in decreasing mortality in severe COVID-19 cases. More studies are needed to validate the benefit of corticosteroids in COVID-19.

This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conducted extensive sets of experiments on two CT image datasets, namely, the SARS-CoV-2 CT-scan and the COVID19-CT. The results show superior performances for our models compared with previous studies. Our best models achieved average accuracy, precision, sensitivity, specificity, and F1-score values of 99.4%, 99.6%, 99.8%, 99.6%, and 99.4% on the SARS-CoV-2 dataset, and 92.9%, 91.3%, 93.7%, 92.2%, and 92.5% on the COVID19-CT dataset, respectively. For better interpretability of the results, we applied visualization techniques to provide visual explanations for the models’ predictions. Feature visualizations of the learned features show well-separated clusters representing CT images of COVID-19 and non-COVID-19 cases. Moreover, the visualizations indicate that our models are not only capable of identifying COVID-19 cases but also provide accurate localization of the COVID-19-associated regions, as indicated by well-trained radiologists.

After contracting COVID-19, a substantial number of individuals develop a Post-COVID-Condition, marked by neurologic symptoms such as cognitive deficits, olfactory dysfunction, and fatigue. Despite this, biomarkers and pathophysiological understandings of this condition remain limited. Employing magnetic resonance imaging, we conduct a comparative analysis of cerebral microstructure among patients with Post-COVID-Condition, healthy controls, and individuals that contracted COVID-19 without long-term symptoms. We reveal widespread alterations in cerebral microstructure, attributed to a shift in volume from neuronal compartments to free fluid, associated with the severity of the initial infection. Correlating these alterations with cognition, olfaction, and fatigue unveils distinct affected networks, which are in close anatomical-functional relationship with the respective symptoms. After contracting COVID-19, a substantial number of individuals develop a Post-COVID-Condition with neurological symptoms. Here, the authors show symptom-specific brain microstructure alterations in these patients, providing insights into the underlying pathophysiology.

The mechanisms by which any upper respiratory virus, including SARS-CoV-2, impairs chemosensory function are not known. COVID-19 is frequently associated with olfactory dysfunction after viral infection, which provides a research opportunity to evaluate the natural course of this neurological finding. Clinical trials and prospective and histological studies of new-onset post-viral olfactory dysfunction have been limited by small sample sizes and a paucity of advanced neuroimaging data and neuropathological samples. Although data from neuropathological specimens are now available, neuroimaging of the olfactory system during the acute phase of infection is still rare due to infection control concerns and critical illness and represents a substantial gap in knowledge. The active replication of SARS-CoV-2 within the brain parenchyma (ie, in neurons and glia) has not been proven. Nevertheless, post-viral olfactory dysfunction can be viewed as a focal neurological deficit in patients with COVID-19. Evidence is also sparse for a direct causal relation between SARS-CoV-2 infection and abnormal brain findings at autopsy, and for trans-synaptic spread of the virus from the olfactory epithelium to the olfactory bulb. Taken together, clinical, radiological, histological, ultrastructural, and molecular data implicate inflammation, with or without infection, in either the olfactory epithelium, the olfactory bulb, or both. This inflammation leads to persistent olfactory deficits in a subset of people who have recovered from COVID-19. Neuroimaging has revealed localised inflammation in intracranial olfactory structures. To date, histopathological, ultrastructural, and molecular evidence does not suggest that SARS-CoV-2 is an obligate neuropathogen. The prevalence of CNS and olfactory bulb pathosis in patients with COVID-19 is not known. We postulate that, in people who have recovered from COVID-19, a chronic, recrudescent, or permanent olfactory deficit could be prognostic for an increased likelihood of neurological sequelae or neurodegenerative disorders in the long term. An inflammatory stimulus from the nasal olfactory epithelium to the olfactory bulbs and connected brain regions might accelerate pathological processes and symptomatic progression of neurodegenerative disease. Persistent olfactory impairment with or without perceptual distortions (ie, parosmias or phantosmias) after SARS-CoV-2 infection could, therefore, serve as a marker to identify people with an increased long-term risk of neurological disease.