Evaluation of lung involvement in COVID-19 pneumonia based on ultrasound images.
Biomed Eng Online
; 20(1): 27, 2021 Mar 20.
Article
in English
| MEDLINE | ID: covidwho-1143220
ABSTRACT
BACKGROUND:
Lung ultrasound (LUS) can be an important imaging tool for the diagnosis and assessment of lung involvement. Ultrasound sonograms have been confirmed to illustrate damage to a person's lungs, which means that the correct classification and scoring of a patient's sonogram can be used to assess lung involvement.METHODS:
The purpose of this study was to establish a lung involvement assessment model based on deep learning. A novel multimodal channel and receptive field attention network combined with ResNeXt (MCRFNet) was proposed to classify sonograms, and the network can automatically fuse shallow features and determine the importance of different channels and respective fields. Finally, sonogram classes were transformed into scores to evaluate lung involvement from the initial diagnosis to rehabilitation. RESULTS ANDCONCLUSION:
Using multicenter and multimodal ultrasound data from 104 patients, the diagnostic model achieved 94.39% accuracy, 82.28% precision, 76.27% sensitivity, and 96.44% specificity. The lung involvement severity and the trend of COVID-19 pneumonia were evaluated quantitatively.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia
/
Ultrasonography
/
COVID-19
/
Lung
Type of study:
Diagnostic study
/
Experimental Studies
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Biomed Eng Online
Journal subject:
Biomedical Engineering
Year:
2021
Document Type:
Article
Affiliation country:
S12938-021-00863-x
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