A novel multimodal fusion framework for early diagnosis and accurate classification of COVID-19 patients using X-ray images and speech signal processing techniques.

BACKGROUND AND OBJECTIVE: COVID-19 outbreak has become one of the most challenging problems for human being. It is a communicable disease caused by a new coronavirus strain, which infected over 375 million people already and caused almost 6 million deaths. This paper aims to develop and design a framework for early diagnosis and fast classification of COVID-19 symptoms using multimodal Deep Learning techniques. METHODS: we collected chest X-ray and cough sample data from open source datasets, Cohen and datasets and local hospitals. The features are extracted from the chest X-ray images are extracted from chest X-ray datasets. We also used cough audio datasets from Coswara project and local hospitals. The publicly available Coughvid DetectNow and Virufy datasets are used to evaluate COVID-19 detection based on speech sounds, respiratory, and cough. The collected audio data comprises slow and fast breathing, shallow and deep coughing, spoken digits, and phonation of sustained vowels. Gender, geographical location, age, preexisting medical conditions, and current health status (COVID-19 and Non-COVID-19) are recorded. RESULTS: The proposed framework uses the selection algorithm of the pre-trained network to determine the best fusion model characterized by the pre-trained chest X-ray and cough models. Third, deep chest X-ray fusion by discriminant correlation analysis is used to fuse discriminatory features from the two models. The proposed framework achieved recognition accuracy, specificity, and sensitivity of 98.91%, 96.25%, and 97.69%, respectively. With the fusion method we obtained 94.99% accuracy. CONCLUSION: This paper examines the effectiveness of well-known ML architectures on a joint collection of chest-X-rays and cough samples for early classification of COVID-19. It shows that existing methods can effectively used for diagnosis and suggesting that the fusion learning paradigm could be a crucial asset in diagnosing future unknown illnesses. The proposed framework supports health informatics basis on early diagnosis, clinical decision support, and accurate prediction.

A 16-Year-Old Boy With Cough and Fever in the Era of COVID-19.

A 16-year-old white boy with a history of chronic lung disease of prematurity, cough-variant asthma, and incidental lung nodules presented to the emergency center in spring 2020 with acute onset dry cough, shortness of breath, and fever. An initial history, gathered from his mother because of the patient's respiratory distress, revealed no recent travel. However, his mother is a health care worker at a hospital, and sick contacts included ongoing contact with a friend with cold-like symptoms. He had a variety of animals at home, including a dog, cats, fish, rodents, and reptiles. He had a history of vaping tobacco products >6 months ago. Fever and respiratory symptoms were associated with fatigue, chest tightness, abdominal pain, and myalgias. On examination, he was ill appearing and had tachycardia, tachypnea, borderline hypoxia with an oxygen saturation of 91% on room air, diminished breath sounds at the lung bases, and unremarkable abdominal examination results. A chest radiograph was consistent with the lung examination, revealing bilateral lower lobe hazy infiltrates. He showed initial improvement for 48 hours with antibiotics, intravenous fluid resuscitation, oxygen via nasal cannula, albuterol, and prednisone. Subsequently, he worsened with persistent high fever, increasing respiratory distress with pulmonary findings, and severe persistent epigastric pain, which added a layer of diagnostic complexity. As this patient's clinical course evolved and further history became available, pulmonary medicine and infectious diseases services were consulted to guide diagnostic evaluation and treatment of this patient early in the era of coronavirus disease 2019.

Opsoclonus-myoclonus syndrome, a post-infectious neurologic complication of COVID-19: case series and review of literature.

Opsoclonus-myoclonus-ataxia syndrome is a heterogeneous constellation of symptoms ranging from full combination of these three neurological findings to varying degrees of isolated individual sign. Since the emergence of coronavirus disease 2019 (COVID-19), neurological symptoms, syndromes, and complications associated with this multi-organ viral infection have been reported and the various aspects of neurological involvement are increasingly uncovered. As a neuro-inflammatory disorder, one would expect to observe opsoclonus-myoclonus syndrome after a prevalent viral infection in a pandemic scale, as it has been the case for many other neuro-inflammatory syndromes. We report seven cases of opsoclonus-myoclonus syndrome presumably parainfectious in nature and discuss their phenomenology, their possible pathophysiological relationship to COVID-19, and diagnostic and treatment strategy in each case. Finally, we review the relevant data in the literature regarding the opsoclonus-myoclonus syndrome and possible similar cases associated with COVID-19 and its diagnostic importance for clinicians in various fields of medicine encountering COVID-19 patients and its complications.

Meningoencephalitis associated with COVID-19: a systematic review.

With the growing number of COVID-19 cases in recent times. significant set of patients with extra pulmonary symptoms has been reported worldwide. Here we venture out to summarize the clinical profile, investigations, and radiological findings among patients with SARS-CoV-2-associated meningoencephalitis in the form of a systemic review. This review was carried out based on the existing PRISMA (Preferred Report for Systematic Review and Meta analyses) consensus statement. The data for this review was collected from four databases: Pubmed/Medline, NIH Litcovid, Embase, and Cochrane library and Preprint servers up till 30 June 2020. Search strategy comprised of a range of keywords from relevant medical subject headings which includes "SARS-COV-2," "COVID-19," and "meningoencephalitis." All peer reviewed, case-control, case report, pre print articles satisfying our inclusion criteria were involved in the study. Quantitative data was expressed in mean ± SD, while the qualitative date in percentages. Paired t test was used for analysing the data based on differences between mean and respective values with a p < 0.05 considered to be statistically significant. A total of 61 cases were included from 25 studies after screening from databases and preprint servers, out of which 54 of them had completed investigation profile and were included in the final analysis. Clinical, laboratory findings, neuroimaging abnormalities, and EEG findings were analyzed in detail. This present review summarizes the available evidences related to the occurrence of meningoencephalitis in COVID-19.

COVID-19 in cancer patients can be challenging to screen in a resource limited setting.

During this COVID-19 pandemic, patients with symptoms such as fever, cough, sore throat, and coryza were advised to have RT-PCR testing for SARS-CoV-2 infection. We described here an elderly female with chronic lymphocytic leukemia, who presented with atypical symptoms that were not directly attributable to COVID-19. This patient was admitted to the non-COVID-19 ward for supportive care. Later, her chest x-ray revealed pneumonia that was confirmed to be COVID-19 by RT-PCR testing several days later. In resource-poor settings where molecular testing results suffered from delays or were altogether unavailable, the use of diagnostic imaging such as a chest x-ray could serve as a quick guide in the assessment and management of these patients especially if the imaging results suggest COVID-19 infection.

Clinical characteristics of COVID-19 in children compared with adults in Shandong Province, China.

AIMS AND BACKGROUND: The COVID-19 outbreak spread in China and is a threat to the world. We reported on the epidemiological, clinical, laboratory, and radiological characteristics of children cases to help health workers better understand and provide timely diagnosis and treatment. METHODS: Retrospectively, two research centers' case series of 67 consecutive hospitalized cases including 53 adult and 14 children cases with COVID-19 between 23 Jan 2020 and 15 Feb 2020 from Jinan and Rizhao were enrolled in this study. Epidemiological, clinical, laboratory, and radiological characteristics of children and adults were analyzed and compared. RESULTS: Most cases in children were mild (21.4%) and conventional cases (78.6%), with mild clinical signs and symptoms, and all cases were of family clusters. Fever (35.7%) and dry cough (21.4%) were described as clinical manifestations in children cases. Dry cough and phlegm were not the most common symptoms in children compared with adults (p = 0.03). In the early stages of the disease, lymphocyte counts did not significantly decline but neutrophils count did in children compared with adults (p = 0.02). There was a lower level of CRP (p = 0.00) in children compared with adults. There were 8 (57.1%) asymptomatic cases and 6 (42.9%) symptomatic cases among the 14 children cases. The age of asymptomatic patients was younger than that of symptomatic patients (p = 0.03). Even among asymptomatic patients, 5 (62.5%) cases had lung injuries including 3 (60%) cases with bilateral involvement, which was not different compared with that of symptomatic cases (p = 0.58, p = 0.74). CONCLUSIONS: The clinical symptoms of children are mild, there is substantial lung injury even among children, but that there is less clinical disease, perhaps because of a less pronounced inflammatory response, and that the occurrence of this pattern appears to inversely correlate with age.