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• W3203349440 abstract "The scale and magnitude of the current SARS-CoV2 or COVID-19 pandemic caused enormous burden to healthcare in addition to the socioeconomics of people and countries. The diagnosis is a key part of the strategies to prevent further spread of the infection and patient treatment. The viral infection can be detected by RT-PCR or by antibody testing for specific IgM. The chest X-ray and CT scan often diagnose the COVID-19 earlier than the laboratory tests. However, when radiologists are overburdened with too many cases it is very difficult to review them accurately and timely. The artificial intelligence (AI) based tools can assist at this step with faster detection of abnormality with comparable accuracy. The AI using neural-network-based models can distinguish COVID-19 from other pneumonia, based on the presence of ground glass opacity (GGO) and other features in X-ray and CT scan images. In addition to the interpretation of the radiological data, AI technology can also help with the optimization of data acquisition, preprocessing of initial slices, restructuring of a 3D image, and augmentation. Combining radiological information with the clinical symptoms and other vital measurements can increase the speed and accuracy of COVID-19 diagnosis. Based on the success of AI tools, we predict that the AI-based diagnostic will soon come bundled with the X-ray and CT operating software. In addition to radiology, the AI can be applied to other related data types from simple PCR data to complex high-dimensional cytometry data for automation and extraction of relevant information." @default.
• W3203349440 created "2021-10-11" @default.
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• W3203349440 date "2021-01-01" @default.
• W3203349440 modified "2023-10-18" @default.
• W3203349440 title "Artificial Intelligence-Mediated Medical Diagnosis of COVID-19" @default.
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• W3203349440 doi "https://doi.org/10.1007/978-981-15-7317-0_3" @default.
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