Background: Whether the sensitivity of Deep Learning (DL) models to screen chest radiographs (CXR) for CoVID-19 can approximate that of radiologists, so that they can be adopted and used if real-time review of CXRs by radiologists is not possible, has not been explored before. Objective: To evaluate the diagnostic performance of a doctor-trained DL model (Svita_DL8) to screen for COVID-19 on CXR, and to compare the performance of the DL model with that of expert radiologists. Materials and Methods: We used a pre-trained convolutional neural network to develop a publicly available online DL model to evaluate CXR examinations saved in .jpeg or .png format. The initial model was subsequently curated and trained by an internist and a radiologist using 1062 chest radiographs to classify a submitted CXR as either normal, COVID-19, or a non-COVID-19 abnormal. For validation, we collected a separate set of 430 CXR examinations from numerous publicly available datasets from 10 different countries, case presentations, and two hospital repositories. These examinations were assessed for COVID-19 by the DL model and by two independent radiologists. Diagnostic performance was compared between the model and the radiologists and the correlation coefficient calculated. Results: For detecting COVID-19 on CXR, our DL model demonstrated sensitivity of 91.5%, specificity of 55.3%, PPV 60.9%, NPV 77.9%, accuracy 70.1%, and AUC 0.73 (95% CI: 0.86, 0.95). There was a significant correlation (r = 0.617, P = 0.000) between the results of the DL model and the radiologists’ interpretations. The sensitivity of the radiologists is 96% and their overall diagnostic accuracy is 90% in this study. Conclusions: The DL model demonstrated high sensitivity for detecting COVID-19 on CXR. Clinical Impact: The doctor trained DL tool Svita_DL8 can be used in resource-constrained settings to quickly triage patients with suspected COVID-19 for further in-depth review and testing.

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Background:Coronavirus is a novel virus which has disrupted life in the past year. While it involves the lungs in the majority and this has been extensively studied, it involves other organ systems. More number of studies need to be focused on the extrapulmonary manifestations of the disease.Objective:To delineate the clinical manifestations of coronavirus disease 2019 (COVID-19) virus on the central and peripheral nervous systems and to assess the risk factors and the outcome of COVID-19 patients with neurological manifestations.Materials and Methods:All patients who were SARS-CoV-2 RNA polymerase chain reaction (PCR) positive were assessed, and detailed clinical history and laboratory findings were collected. Data was analyzed using percentage, mean, and frequency.Results:Out of 864 patients, 17 (N= 17, 1.96%) had neurological manifestations. Twelve out of 17 had comorbid conditions. Patients had diverse presentations ranging from acute cerebrovascular accident to paraplegia and encephalopathy. Ten (58.8%) patients presented with acute cerebrovascular accidents. Of the patients who developed stroke, five (50%) died.Conclusions:COVID-19 usually presents as a respiratory disease. The neurological manifestations of COVID-19 are not uncommon. One should be aware of a wide spectrum of neurological signs and symptoms of COVID-19 for early diagnosis and treatment for preventing mortality and morbidity.

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