The rapid evolution of the novel coronavirus SARS-CoV-2 pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone. We develop a novel patient-level algorithm to aggregate the chest CT volume into a 2D representation that can be easily integrated with clinical metadata to distinguish Novel Coronavirus Pneumonia (COVID-19+) from other cases of viral pneumonia and normal healthy chest CT volumes with state-of-the-art performance. Furthermore, we present a multitask model for joint segmentation of different classes of pulmonary lesions present in COVID-19 infected lungs that can outperform individual segmentation models for each task. We directly compare this multitask segmentation approach to combining feature-agnostic volumetric CT classification feature maps with clinical metadata for predicting mortality. These approaches enable the automated extraction of clinically relevant features from chest CT volumes for risk stratification of COVID-19+ patients.<\/p>\n","protected":false},"excerpt":{"rendered":"
The rapid evolution of the novel coronavirus SARS-CoV-2 pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from computed tomography (CT) imaging data. 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