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Background: Easy availability, low cost, and low radiation exposure make chest radiography an ideal modality for coronavirus disease 2019 (COVID-19) detection. Objective(s): In this study, we propose the use of an artificial intelligence (AI) algorithm to automatically detect abnormalities associated with COVID-19 on chest radiographs. We aimed to evaluate the performance of the algorithm against the interpretation of radiologists to assess its utility as a COVID-19 triage tool. Material(s) and Method(s): The study was conducted in collaboration with Kaushalya Medical Trust Foundation Hospital, Thane, Maharashtra, between July and August 2020. We used a collection of public and private datasets to train our AI models. Specificity and sensitivity measures were used to assess the performance of the AI algorithm by comparing AI and radiology predictions using the result of the reverse transcriptase-polymerase chain reaction as reference. We also compared the existing open-source AI algorithms with our method using our private dataset to ascertain the reliability of our algorithm. Result(s): We evaluated 611 scans for semantic and non-semantic features. Our algorithm showed a sensitivity of 77.7% and a specificity of 75.4%. Our AI algorithm performed better than the radiologists who showed a sensitivity of 75.9% and specificity of 75.4%. The open-source model on the same dataset showed a large disparity in performance measures with a specificity of 46.5% and sensitivity of 91.8%, thus confirming the reliability of our approach. Conclusion(s): Our AI algorithm can aid radiologists in confirming the findings of COVID-19 pneumonia on chest radiography and identifying additional abnormalities and can be used as an assistive and complementary first-line COVID-19 triage tool.Copyright © Cancer Research, Statistics, and Treatment.
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Background: The activity level of the 2019 novel coronavirus (2019-nCoV) or coronavirus disease 2019 (COVID-19), as it is now called, is considered low. Despite early preventive lockdown measures and a massive vaccination drive, almost the entire adult population in India will have been vaccinated at least once by the beginning of 2022 (2,072,946,593 till 11 August 2022). There is still concern about a pan-India outbreak and threat due to newly emerging pathogenic strains. The goal of this study is to find out how common various presenting complaints are in COVID-19 patients as well as how comorbidities affect the severity of the illness. Methods: This cross-sectional observational study was conducted from December 2020 to January 2021 at a tertiary care hospital's department of internal medicine in North India. The study included 237 patients who were COVID-19-positive and were admitted to our hospital after providing informed consent. They were classified into three groups: mild, moderate, and severe. Results: Fever was the most common presenting symptom, affecting 84.4% of the population, while diarrhoea was the least common, affecting only 3.4% of the population. Fever, cough, sore throat, headache, and breathlessness were significantly correlated with the severity of the illness. Gastrointestinal symptoms like diarrhoea did not have any significant correlation with the severity of the illness. The severity of illness was statistically related to comorbidities such as hypertension, diabetes, coronary artery disease, chronic kidney disease, and chronic obstructive pulmonary disease. Conclusion: Males were more likely to develop more serious illnesses. However, the correlation was not statistically significant. The number of comorbid conditions and the severity of the illness were found to have a fair and significant relationship. None of the diarrhoea symptoms were related to the severity of the illness.
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Background: Chest computed tomography (CT) is a readily available diagnostic test that can aid in the detection and assessment of the severity of the coronavirus disease 2019 (COVID-19). Given the wide community spread of the disease, it can be difficult for radiologists to differentiate between COVID-19 and non-COVID-19 pneumonia, especially in the oncological setting. Objective: This study was aimed at developing an artificial intelligence (AI) algorithm that could automatically detect COVID-19-related abnormalities from chest CT images and could serve as a diagnostic tool for COVID-19. In addition, we assessed the performance and accuracy of the algorithm in differentiating COVID-19 from non-COVID-19 lung parenchyma pathologies. Materials and Methods: A total of 1581 chest CT images of individuals affected with COVID-19, individuals affected with non-COVID-19 pathologies, and healthy individuals were included in this study. All the digital images of COVID-19-positive cases were obtained from web databases available in the public domain. About 60% of the data were used for training and validation of the algorithm, and the remaining 40% were used as a test set. A single-stage deep learning architecture based on the RetinaNet framework was used as the AI model for image classification. The performance of the algorithm was evaluated using various publicly available datasets comprising patients with COVID-19, patients with pneumonia, other lung diseases (underlying malignancies), and healthy individuals without any abnormalities. The specificity, sensitivity, and area under the receiver operating characteristic curve (AUC) were measured to estimate the effectiveness of our method. Results: The semantic and non-semantic features of the algorithm were analyzed. For the COVID-19 classification network, the sensitivity, specificity, accuracy, and AUC were 0.92 (95% confidence interval [CI]: 0.85-0.97), 0.995 (95% CI: 0.984-1.0), 0.972 (95% CI: 0.952-0.988), and 0.97 (95% CI: 0.945-0.986), respectively. For the non-COVID classification network, the sensitivity, specificity, and accuracy were 0.931 (95% CI: 0.88-0.975), 0.94 (95% CI: 0.90-0.974), and 0.935 (95% CI: 0.90, 0.965), respectively. Conclusion: The AI algorithm developed in our study can detect COVID-19 abnormalities from CT images with high sensitivity and specificity. Our AI algorithm can be used for the early detection and timely management of patients with COVID-19. © 2021 Cancer Research, Statistics, and Treatment ;Published by Wolters Kluwer - Medknow.