TY - GEN
T1 - A covid-19 patient severity stratification using a 3D convolutional strategy on CT-Scans
AU - Rodriguez, Jefferson
AU - Romo-Bucheli, David
AU - Sierra, Franklin
AU - Valenzuela, Diana
AU - Valenzuela, Carolina
AU - Vasquez, Lina
AU - Camacho, Paul
AU - Mantilla, Daniel
AU - Martinez, Fabio
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/4/13
Y1 - 2021/4/13
N2 - 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 < 0.01) for all the compared severity groups.
AB - 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 < 0.01) for all the compared severity groups.
KW - 3D-CNN networks
KW - COVID-19
KW - Deep learning
KW - Severity
KW - Tomography
UR - http://www.scopus.com/inward/record.url?scp=85107232319&partnerID=8YFLogxK
U2 - 10.1109/ISBI48211.2021.9434154
DO - 10.1109/ISBI48211.2021.9434154
M3 - Libros de Investigación
AN - SCOPUS:85107232319
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1665
EP - 1668
BT - 2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PB - IEEE Computer Society
T2 - 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
Y2 - 13 April 2021 through 16 April 2021
ER -