An AI-based auxiliary empirical antibiotic therapy model for children with bacterial pneumonia using low-dose chest CT images
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 and methods 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..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:39 |
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| Enthalten in: |
Radiation medicine - 39(2021), 10 vom: 08. Juni, Seite 973-983 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Mudan [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| BKL: | |
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| Themen: |
Bacterial pneumonia |
| Anmerkungen: |
© The Author(s) 2021 |
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| doi: |
10.1007/s11604-021-01136-2 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
SPR045215103 |
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| 520 | |a 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 and methods 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. | ||
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| 700 | 1 | |a Yu, Siwei |e verfasserin |4 aut | |
| 700 | 1 | |a Yin, Xuntao |e verfasserin |4 aut | |
| 700 | 1 | |a Zeng, Xianchun |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Xinfeng |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, ZhiYan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xiaoyong |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Chencui |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Rongpin |e verfasserin |4 aut | |
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