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The coronavirus disease 2019 (COVID-19) pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is causing considerable morbidity and mortality worldwide. Multiple reports have suggested that patients with heart failure (HF) are at a higher risk of severe disease and mortality with COVID-19. Moreover, evaluating and treating HF patients with comorbid COVID-19 represents a formidable clinical challenge as symptoms of both conditions may overlap and they may potentiate each other. Limited data exist regarding comprehensive management of HF patients with concomitant COVID-19. Since these issues pose serious new challenges for clinicians worldwide, HF specialists must develop a structured approach to the care of patients with COVID-19 and be included early in the care of these patients. Therefore, the Heart Failure Association of the European Society of Cardiology and the Chinese Heart Failure Association & National Heart Failure Committee conducted web-based meetings to discuss these unique clinical challenges and reach a consensus opinion to help providers worldwide deliver better patient care. The main objective of this position paper is to outline the management of HF patients with concomitant COVID-19 based on the available data and personal experiences of physicians from Asia, Europe and the United States. [ABSTRACT FROM AUTHOR]

Background: The COVID‐19 pandemic impacted on the provision of care and routine activity of all National Health Service (NHS) services. While General Practitioner referrals to memory services in England have returned to pre‐pandemic levels, the estimated dementia diagnosis rate (DDR) fell by 5.4% between March 2020 and February 2023. Methods: In this paper we explore whether this reduction is accurate or is an artefact of the way the NHS collects data. Results: We explore the processes that may have affected national dementia diagnosis rates during and following the COVID‐19 pandemic. Conclusions: We discuss what action could be taken to improve the DDR in the future. Key points: Despite General Practitioner (GP) referrals to memory services in England returning to levels seen before the pandemic, there was a decline of 5.4% in the estimated dementia diagnosis rate (DDR) from March 2020 to February 2023.This paper explores the factors which may have affected the national DDR reduction. These include a backlog in dementia referrals, a reduction in coding of diagnoses, or a decrease in true dementia prevalence secondary to excess COVID‐19 deaths which has yet to be reflected in the DDR denominator.Further work is suggested to accurately capture dementia prevalence in the United Kingdom (UK). These include an up‐to‐date multicentre population‐based cohort study and adjusting the DDR denominator for factors known to affect dementia susceptibility such as deprivation, rurality, and ethnicity. [ABSTRACT FROM AUTHOR]

Since the first case of COVID‐19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID‐19 and COVID‐19 findings from computed tomography images using pre‐trained models using two different datasets. COVID‐19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy‐paving pattern, ground‐glass opacity, ground‐glass opacity and consolidation, ground‐glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre‐trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient‐weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID‐19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID‐19 in the lungs was an innovative and successful approach. [ABSTRACT FROM AUTHOR]

Recently, the whole world was hit by COVID‐19 pandemic that led to health emergency everywhere. During the peak of the early waves of the pandemic, medical and healthcare departments were overwhelmed by the number of COVID‐19 cases that exceeds their capacity. Therefore, new rules and techniques are urgently required to help in receiving, filtering and diagnosing patients. One of the decisive steps in the fight against COVID‐19 is the ability to detect patients early enough and selectively put them under special care. Symptoms of this disease can be observed in chest X‐rays. However, it is sometimes difficult and tricky to differentiate "only" pneumonia patients from COVID‐19 patients. Machine‐learning can be very helpful in carrying out this task. In this paper, we tackle the problem of COVID‐19 diagnostics following a data‐centric approach. For this purpose, we construct a diversified dataset of chest X‐ray images from publicly available datasets and by applying data augmentation techniques. Then, we employ a transfer learning approach based on a pre‐trained convolutional neural network (DenseNet‐169) to detect COVID‐19 in chest X‐ray images. In addition to that, we employ Gradient‐weighted Class Activation Mapping (GradCAM) to provide visual inspection and explanation of the predictions made by our deep learning model. The results were evaluated against various metrics such as sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) and the confusion matrix. The resulting models has achieved an average detection accuracy close to 98.82%. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Aim: The COVID‐19 pandemic led to widespread disruption of colorectal cancer services during 2020. Established cancer referral pathways were modified in response to reduced diagnostic availability. The aim of this paper is to assess the impact of COVID‐19 on colorectal cancer referral, presentation and stage. Methods: This was a single centre, retrospective cohort study performed at a tertiary referral centre. Patients diagnosed and managed with colorectal adenocarcinoma between January and December 2020 were compared with patients from 2018 and 2019 in terms of demographics, mode of presentation and pathological cancer staging. Results: In all, 272 patients were diagnosed with colorectal adenocarcinoma during 2020 compared with 282 in 2019 and 257 in 2018. Patients in all years were comparable for age, gender and tumour location (P > 0.05). There was a significant decrease in urgent suspected cancer referrals, diagnostic colonoscopy and radiological imaging performed between March and June 2020 compared with previous years. More patients presented as emergencies (P = 0.03) with increased rates of large bowel obstruction in 2020 compared with 2018–2019 (P = 0.01). The distribution of TNM grade was similar across the 3 years but more T4 cancers were diagnosed in 2020 versus 2018–2019 (P = 0.03). Conclusion: This study demonstrates that a relatively short‐term impact on the colorectal cancer referral pathway can have significant consequences on patient presentation leading to higher risk emergency presentation and surgery at a more advanced stage. It is therefore critical that efforts are made to make this pathway more robust to minimize the impact of other future adverse events and to consolidate the benefits of earlier diagnosis and treatment. [ABSTRACT FROM AUTHOR]