Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans

Foo, Michelle Xiao-Lin; Kim, Seong-Tae; Paschali, Magdalini; Goli, Leili; Burian, Egon; Makowski, Marcus R.; Braren, Rickmer; Navab, Nassir; Wendler, Thomas

doi:10.48550/arxiv.2110.00948

Cited by 1 publication

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“…For the detection of pneumonia regions, the model had a sensitivity of 95% with a specificity of 84%. The study in [ 137 ] developed a deep learning model whose primary goal was to track the disease progression of COVID-19 through the use of longitudinal CT imaging. FC-DenseNet56 was used to process slices and a 2.5D model was for the two scan time points.…”

Section: Covid-19 Prognostic and Longitudinal Modelsmentioning

confidence: 99%

A Comprehensive Review of Machine Learning Used to Combat COVID-19

et al. 2022

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Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.

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Section: Covid-19 Prognostic and Longitudinal Modelsmentioning

confidence: 99%