Article ; Online: COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring.
IEEE journal of biomedical and health informatics
2021 Volume 25, Issue 6, Page(s) 1892–1903
Abstract: This work estimates the severity of pneumonia in COVID-19 patients and reports the findings of a longitudinal study of disease progression. It presents a deep learning model for simultaneous detection and localization of pneumonia in chest Xray (CXR) ... ...
Abstract | This work estimates the severity of pneumonia in COVID-19 patients and reports the findings of a longitudinal study of disease progression. It presents a deep learning model for simultaneous detection and localization of pneumonia in chest Xray (CXR) images, which is shown to generalize to COVID-19 pneumonia. The localization maps are utilized to calculate a "Pneumonia Ratio" which indicates disease severity. The assessment of disease severity serves to build a temporal disease extent profile for hospitalized patients. To validate the model's applicability to the patient monitoring task, we developed a validation strategy which involves a synthesis of Digital Reconstructed Radiographs (DRRs - synthetic Xray) from serial CT scans; we then compared the disease progression profiles that were generated from the DRRs to those that were generated from CT volumes. |
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MeSH term(s) | COVID-19/diagnostic imaging ; COVID-19/pathology ; COVID-19/virology ; Case-Control Studies ; Humans ; Monitoring, Physiologic/methods ; Pneumonia, Viral/diagnostic imaging ; Pneumonia, Viral/pathology ; Pneumonia, Viral/virology ; Radiography, Thoracic ; SARS-CoV-2/isolation & purification ; Severity of Illness Index |
Language | English |
Publishing date | 2021-06-03 |
Publishing country | United States |
Document type | Journal Article |
ZDB-ID | 2695320-1 |
ISSN | 2168-2208 ; 2168-2194 |
ISSN (online) | 2168-2208 |
ISSN | 2168-2194 |
DOI | 10.1109/JBHI.2021.3069169 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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