Data Driven Estimation of Covid-19 Prognosis
19th IEEE International Symposium on Biomedical Imaging, ISBI 2022
; 2022-March, 2022.
Article
in English
| Scopus | ID: covidwho-1846115
ABSTRACT
Continuous spread of novel coronavirus (COVID-19) and availability of limited resources force the severity-based allocation of resources. While it is essential to have a reliable severity assessment method, it is even more critical to have a prognosis model to estimate infection progress in individuals. An accurate estimate of infection progression would naturally help in optimized treatment and morbidity reduction. We aim at the prognosis of the COVID-19 infections including, ground-glass opacities, consolidation, and pleural effusion, from the longitudinal chest X-ray (CXR) images of the patient. For this purpose, we first propose a learning-based framework that predicts infection type from a given CXR image. This helps in finding low dimensional embeddings of CXR images, which we use in a recurrent learning framework to predict the type of infection for the subsequent days. We achieve a test AUC of 0.85 for infection type prediction and a test AUC of 0.88 for prognosis on the benchmark COVID-19 dataset. © 2022 IEEE.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
19th IEEE International Symposium on Biomedical Imaging, ISBI 2022
Year:
2022
Document Type:
Article
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