A Covid-19 Patient Severity Stratification using a 3D Convolutional Strategy on CT-Scans

This work introduces a 3D deep learning methodology to stratify patients according to the severity of lung infection caused by COVID-19 disease on computerized tomography images (CT). A set of volumetric attention maps were also obtained to explain the results and support the diagnostic tasks. The v...

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Bibliographic Details
Main Authors: Rodriguez, Jefferson, Romo-Bucheli, David, Sierra, Franklin, Valenzuela, Diana, Valenzuela, Carolina, Vasquez, Lina, Camacho, Paul, Mantilla, Daniel, Martinez, Fabio
Format: Conference Proceeding
Language:English
Subjects:
3D-CNN networks
Computed tomography
COVID-19
Deep learning
Lung
Protocols
severity
Task analysis
Three-dimensional displays
tomography
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Summary:This work introduces a 3D deep learning methodology to stratify patients according to the severity of lung infection caused by COVID-19 disease on computerized tomography images (CT). A set of volumetric attention maps were also obtained to explain the results and support the diagnostic tasks. The validation of the approach was carried out on a dataset composed of 350 patients, diagnosed by the RT-PCR assay either as negative (control - 175) or positive (COVID-19 - 175). Additionally, the patients were graded (0-25) by two expert radiologists according to the extent of lobar involvement. These gradings were used to define 5 COVID-19 severity categories. The model yields an average 60% accuracy for the multi-severity classification task. Additionally, a set of Mann Whitney U significance tests were conducted to compare the severity groups. Results show that patients in different severity groups have significantly different severity scores (p
ISSN:1945-8452
DOI:10.1109/ISBI48211.2021.9434154