Deep learning-based COVID-19 triage tool: An observational study on an X-ray dataset

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.

Novel artificial intelligence algorithm for automatic detection of COVID-19 abnormalities in computed tomography images

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.

Traces of Time through Space: Advantages of Creating Complex Canvases in Collaborative Meetings

Technology have long been a partner of workplace meeting facilitation. The recent outbreak of COVID-19 and the cautionary measures to reduce its spread have made it more prevalent than ever before in the form of online-meetings. In this paper, we recount our experiences during weekly meetings in three modalities: using SAGE2 - a collaborative sharing software designed for large displays - for co-located meetings, using a conventional projector for co-located meetings, and using the Zoom video-conferencing tool for distributed meetings. We view these meetings through the lens of effective meeting attributes and share ethnographic observations and attitudinal survey conducted in our research lab. We discuss patterns of content sharing, either sequential, parallel, or semi-parallel, and the potential advantages of creating complex canvases of content. We see how the SAGE2 tool affords parallel content sharing to create complex canvases, which represent queues of ideas and contributions (past, present, and future) using the space on a large display to suggest the progression of time through the meeting. © 2021 ACM.

Computational Intelligence in Vaccine Design Against COVID-19

The pandemic Novel coronavirus disease (COVID-19) was aggressively expanding throughout the world, and no effective vaccines and drugs are available. Giant pharmaceutical industries and researchers use computer intelligence coupled with bioinformatics knowledge to accelerate the development process of designing an effective vaccine against SARS-CoV-2, a time-consuming, complicated, intricate, and complex process. Supercomputers are used to give power to the Artificial Intelligence (AI) and Machine Learning (ML) assistance for structural modeling of unresolved protein, molecular dynamics simulation (MD) of the modeled protein structure, target finding, selection of B and T cell epitopes and simulations study for vaccine development. In this chapter, we described an in-depth overview on the use and impact of various revolutionary and game-changing technology of computer intelligence like AI and ML which with the guide of computational biology, bioinformatics, structural biology, and genomics paved the way in understanding, design, and development of vaccines at a much diminished time and minimal cost. Various software and tools used in the developmental process are also consolidated here. Finally, the limitations and future prospects of overcoming the global crisis and tackling pandemics with the help of computational intelligence are speculated here. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.