Automated CT Lung Density Analysis of Viral Pneumonia and Healthy Lungs Using Deep Learning-Based Segmentation, Histograms and HU Thresholds.
artificial intelligence
computed tomography
histogram analysis
imaging biomarker
viral pneumonia
Journal
Diagnostics (Basel, Switzerland)
ISSN: 2075-4418
Titre abrégé: Diagnostics (Basel)
Pays: Switzerland
ID NLM: 101658402
Informations de publication
Date de publication:
21 Apr 2021
21 Apr 2021
Historique:
received:
26
03
2021
revised:
16
04
2021
accepted:
17
04
2021
entrez:
30
4
2021
pubmed:
1
5
2021
medline:
1
5
2021
Statut:
epublish
Résumé
CT patterns of viral pneumonia are usually only qualitatively described in radiology reports. Artificial intelligence enables automated and reliable segmentation of lungs with chest CT. Based on this, the purpose of this study was to derive meaningful imaging biomarkers reflecting CT patterns of viral pneumonia and assess their potential to discriminate between healthy lungs and lungs with viral pneumonia. This study used non-enhanced and CT pulmonary angiograms (CTPAs) of healthy lungs and viral pneumonia (SARS-CoV-2, influenza A/B) identified by radiology reports and RT-PCR results. After deep learning segmentation of the lungs, histogram-based and threshold-based analyses of lung attenuation were performed and compared. The derived imaging biomarkers were correlated with parameters of clinical and biochemical severity (modified WHO severity scale; c-reactive protein). For non-enhanced CTs (
Identifiants
pubmed: 33919094
pii: diagnostics11050738
doi: 10.3390/diagnostics11050738
pmc: PMC8143124
pii:
doi:
Types de publication
Journal Article
Langues
eng
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