An AI-based auxiliary empirical antibiotic therapy model for children with bacterial pneumonia using low-dose chest CT images.
Jpn J Radiol
; 39(10): 973-983, 2021 Oct.
Article
in English
| MEDLINE | ID: covidwho-1530376
ABSTRACT
PURPOSE:
To construct an auxiliary empirical antibiotic therapy (EAT) multi-class classification model for children with bacterial pneumonia using radiomics features based on artificial intelligence and low-dose chest CT images. MATERIALS ANDMETHODS:
Data were retrospectively collected from children with pathogen-confirmed bacterial pneumonia including Gram-positive bacterial pneumonia (122/389, 31%), Gram-negative bacterial pneumonia (159/389, 41%) and atypical bacterial pneumonia (108/389, 28%) from January 1 to June 30, 2019. Nine machine-learning models were separately evaluated based on radiomics features extracted from CT images; three optimal submodels were constructed and integrated to form a multi-class classification model.RESULTS:
We selected five features to develop three radiomics submodels a Gram-positive model, a Gram-negative model and an atypical model. The comprehensive radiomics model using support vector machine method yielded an average area under the curve (AUC) of 0.75 [95% confidence interval (CI), 0.65-0.83] and accuracy (ACC) of 0.58 [sensitivity (SEN), 0.57; specificity (SPE), 0.78] in the training set, and an average AUC of 0.73 (95% CI 0.61-0.79) and ACC of 0.54 (SEN, 0.52; SPE, 0.75) in the test set.CONCLUSION:
This auxiliary EAT radiomics multi-class classification model was deserved to be researched in differential diagnosing bacterial pneumonias in children.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Bacterial
/
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
/
Observational study
Limits:
Child
/
Humans
Language:
English
Journal:
Jpn J Radiol
Journal subject:
Diagnostic Imaging
/
Radiology
/
Radiotherapy
Year:
2021
Document Type:
Article
Affiliation country:
S11604-021-01136-2
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