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An AI-based auxiliary empirical antibiotic therapy model for children with bacterial pneumonia using low-dose chest CT images.
Zhang, Mudan; Yu, Siwei; Yin, Xuntao; Zeng, Xianchun; Liu, Xinfeng; Shen, ZhiYan; Zhang, Xiaoyong; Huang, Chencui; Wang, Rongpin.
  • Zhang M; Guizhou University, School of Medicine, Guiyang, 550000, Guizhou Province, China.
  • Yu S; School of Clinical Medicine, Guizhou Medical University, No. 9 Beijing Road, Yunyan District, Guiyang, 550004, Guizhou Province, China.
  • Yin X; School of Clinical Medicine, Guizhou Medical University, No. 9 Beijing Road, Yunyan District, Guiyang, 550004, Guizhou Province, China.
  • Zeng X; Smart Hospital Construction Office, Guizhou Provincial People's Hospital, No. 83 Zhongshan East Road, Nanming District, Guiyang, 550002, Guizhou Province, China.
  • Liu X; Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou Province, China.
  • Shen Z; Guizhou Provincial Key Laboratory of Intelligent Medical Image Analysis and Precision Diagnosis, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou Province, China.
  • Zhang X; Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou Province, China.
  • Huang C; Guizhou Provincial Key Laboratory of Intelligent Medical Image Analysis and Precision Diagnosis, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou Province, China.
  • Wang R; Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou Province, China.
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 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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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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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