ArticleJapanese journal of radiology2021
An AI-based auxiliary empirical antibiotic therapy model for children with bacterial pneumonia using low-dose chest CT images.
Article in Japanese journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Artificial intelligence in the diagnosis and prognosis of pediatric bacterial pneumonia: current advances and challenges.Current opinion in pediatrics · 2026Review
- A Cohort Study of Pediatric Severe Community-Acquired Pneumonia Involving AI-Based CT Image Parameters and Electronic Health Record Data.Infectious diseases and therapy · 2025Article
- Computed tomography-based radiomics prediction model for differentiating invasive pulmonary aspergillosis andFrontiers in cellular and infection microbiology · 2025Article
- Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Review
- A Deep Learning Model for the Diagnosis and Discrimination of Gram-Positive and Gram-Negative Bacterial Pneumonia for Children Using Chest Radiography Images and Clinical Information.Infection and drug resistance · 2023Article
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
purposeTo 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
methodsData 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.
resultsWe 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.
conclusionThis auxiliary EAT radiomics multi-class classification model was deserved to be researched in differential diagnosing bacterial pneumonias in children.
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