INTRODUCTION
Globally, pneumonia is a leading cause of hospitalization and mortality in children under 5 years of age [1]. This condition is most often caused by bacterial or viral pathogens. These infections require different treatments: antibiotics for bacterial cases and supportive care for viral ones [2▪▪].
However, accurately identifying the cause of community-acquired pneumonia remains clinically difficult. Many common diagnostic markers, such as consolidation on chest X-rays (CXRs) or elevated inflammatory markers, cannot reliably distinguish between viral and bacterial origins [3–5]. Microbiological culture of respiratory specimens is the traditional gold standard for diagnosis, but it has limited sensitivity, difficulty in obtaining quality specimens, and long processing times [6].
Machine learning, a core domain of artificial intelligence, allows computational models to learn from data, conduct statistical analysis, and produce automated decisions. Convolutional neural network (CNN) is a specialized form of machine learning that draws inspiration from neural networks. It processes information through multiple layers to identify complex patterns [7,8].
In healthcare, artificial intelligence assists in tasks like detecting rare diseases and predicting infectious disease outbreaks with public health relevance [9]. For pediatric bacterial pneumonia, artificial intelligence has shown strong results in enhancing medical imaging analysis and creating predictive models [10,11]. This review explores the use of artificial intelligence in diagnosing and predicting the disease, focusing on pathogen identification, severity assessment, and biomarker discovery (Fig.
1).
AUTOMATED DIAGNOSIS FROM MEDICAL IMAGING
Pneumonia represents a significant contributor to the pediatric disease burden, associated with considerable morbidity and mortality. Challenges in identifying causative pathogens often lead to indiscriminate antibiotic use, which in turn accelerates the spread of antimicrobial resistance [12]. Consequently, early and accurate pathogen detection, particularly distinguishing between bacterial and viral pneumonias, is crucial for improving clinical outcomes in children.
Chest radiography remains the primary imaging modality for detecting pediatric pneumonia [13]. However, substantial overlap in imaging manifestations across different pneumonia types complicates differentiation using conventional methods [14]. Artificial intelligence can perform classifications that are difficult for human experts to perform.
In particular, the development of CNN layers has significantly improved the ability to classify images and detect objects in images [15]. These are multiple processing layers to which image analysis filters, or convolutions, are applied. The abstracted representation of images within each layer is constructed by systematically convolving multiple filters across the image, producing a feature map that is used as input to the following layer.
This architecture makes it possible to process images in the form of pixels as input and to give the desired classification as output [16].
To address the diagnostic challenge caused by overlapping imaging features of bacterial and viral pneumonia in children, ML models can be trained on large-scale pediatric CXR datasets. For example, the Guangzhou Pediatric Pneumonia dataset, developed by Kermany et al. [16], is a publicly available pediatric CXR collection. It contains 5856 orthotopic radiographs of children between 1 and 5 years old.
These models learn to automatically identify subtle discriminative imaging patterns and have successfully achieved multiclass classification into normal, viral, and bacterial categories [17,18,19▪]. Beyond lesion detection, machine learning applications now support treatment decisions. For instance, a study has used low-dose chest CT images to develop machine learning based radiomics models.
The model initially extracts hundreds of subtle radiomic features from low-dose chest CT images, which are difficult to quantify by visual inspection. Following feature dimensionality reduction, high-dimensional features are further refined to select those most relevant to the pathogenic etiology. Finally, a predictive classifier is constructed based on these selected features, that distinguish among Gram-positive, Gram-negative, and atypical bacterial pathogens.
This capability offers an objective reference to guide empirical antibiotic selection in pediatric bacterial pneumonia [20,21].
Currently, electronic clinical decision support tools serve as a means to align patient care with guideline recommendations [22,23]. However, in pediatric pneumonia, their application faces limitations in incorporating imaging studies such as chest radiographs into electronic algorithms. Natural language processing (NLP) is a class of machine learning which uses rule-based algorithms to convert unstructured text into encoded data.
In a retrospective study involving 829 751 imaging cases from six hospitals, Nancy Rixe et al. [24] employed NLP tools to enable rapid interpretation of chest radiograph reports and integrate coded results into electronic clinical decision support systems. Their model demonstrated high sensitivity (93.5%) and specificity (98.8%), improving both the efficiency of pneumonia identification and the capacity for epidemiological surveillance [24,25].
In recent years, lung ultrasonography (LUS) has advanced rapidly and is now widely used in the evaluation of pulmonary diseases in neonates and children [26]. Compared with CXRs, LUS offers several advantages that are particularly beneficial for the pediatric population: it involves no ionizing radiation, is relatively low-cost, is portable and accessible in various settings, and can be performed immediately as a bedside examination [27]. LUS has demonstrated high sensitivity and negative predictive value for pneumonia.
It enables quantification of lung disease severity and outcomes [28▪], helps differentiate between viral and bacterial pneumonia, and shows potential in reducing antibiotic misuse [27,29]. Reinforcement learning is a subset of ML that facilitates the acquisition and interpretation of ultrasound images [30]. In cardiac ultrasound, for instance, deep learning techniques assist in guiding standard view acquisition, enhancing image quality, and automating assessments such as left ventricular ejection fraction estimation [31].
Moreover, artificial intelligence has shown high sensitivity in aiding LUS-based detection of pneumothorax [32]. Despite this promise, research on artificial intelligence assisted lung ultrasound for diagnosing bacterial pneumonia in children remains limited.
EARLY PREDICTION OF SEVERE PNEUMONIA WITH MACHINE LEARNING
Pneumonia remains a major cause of childhood mortality worldwide. Therefore, early identification of high-risk patients is crucial to improving clinical outcomes [33]. Machine learning technologies help build accurate predictive models by integrating multisource data, thereby offering personalized risk assessment.
For example, prognostic models using inflammatory markers and cell counts from routine blood tests enable early risk stratification for severe pneumonia in children [34–36]. These data-driven methods can reveal complex, nonlinear relationships among clinical variables, offering clinicians objective decision support. In ICUs, machine learning models have been applied to dynamically assess mortality risk by analyzing admission vital signs, laboratory results, and complications.
Such models assist in rational resource allocation and support the design of individualized treatment plans [37]. At the same time, researchers are developing generalizable risk assessment tools to address global public health needs. One multinational study created and validated a simple tool to identify the risk of in-hospital pneumonia mortality in children aged 2–59 months.
By incorporating easily accessible variables such as age, nutritional status, and clinical signs, this tool shows strong potential for use in resource-limited settings [38].
INTEGRATED DIAGNOSIS USING MULTIMODAL DATA
Multimodal machine learning models demonstrate considerable promise in pediatric bacterial pneumonia diagnosis, pathogen identification, and prognosis prediction. By integrating imaging data with clinical information from electronic health records and laboratory biomarkers, these models significantly advance the feasibility of precision medicine in this domain [39–41]. A recent cohort study developed a predictive model for severe community-acquired pneumonia in children by integrating artificial intelligence derived chest CT parameters, including consolidation extent and ground-glass opacity features, with clinically adjusted electronic health record data such as age, white blood cell count, neutrophil and lymphocyte counts, creatinine levels, and the presence of wheezing.
Radiomics enables quantitative evaluation of pulmonary lesion severity, thereby compensating for the limitations of relying solely on clinical indicators or subjective imaging interpretation. This approach enhances the objectivity of diagnosis and the capacity for early clinical warning [42▪▪].
ARTIFICIAL INTELLIGENCE DRIVEN DISCOVERY OF POTENTIAL BIOMARKERS
Biomarkers are quantifiable indicators of biological processes, pathogenic states, or therapeutic responses. They provide valuable tools for the early identification of severe pneumonia and for distinguishing between viral and bacterial causes [43]. Furthermore, using predictive or monitoring biomarkers can significantly improve treatment outcomes [44].
Conventional assays like ELISA and PCR are typically limited to detecting one or a few predefined targets, such as specific proteins or genes. This targeted approach makes it difficult to identify novel biomarker combinations or those involving complex multidimensional interactions [45,46]. In contrast, artificial intelligence algorithms can integrate multiomics data to uncover new diagnostic and prognostic biomarkers [47].
Artificial intelligence enhanced multiomics analyses show considerable promise for etiological differentiation. Research indicates that integrating proteomics, metabolomics, and lipidomics data can effectively distinguish bacterial from viral pneumonia [48,49▪]. In pediatric patients, certain blood protein signatures have proven reliable for identifying bacterial infections.
These markers, which include HP, LCN2, and S100A9, are associated with neutrophil degranulation [50]. Computational biology methods also contribute to this field by enabling the design of optimized antimicrobial peptides. Using in silico site-directed mutagenesis, researchers can create novel pathways for developing diagnostic markers [51].
In prognosis prediction and critical illness identification, artificial intelligence technologies also show considerable promise. Zhongshu Kuang et al. [52] analyzed the proteomic and metabolomic data of patients with pneumonia at the time of admission, and found that in the severe pneumonia group, the levels of sphingosine-1-phosphate (S1P) and apolipoproteins (APOC1 and APOA2) were downregulated, while the level of S100P was significantly upregulated. This study may provide a possibility for elucidating the complexity of pneumonia severity [52].
Furthermore, Clarissa Valim et al. [53] analyzed the proteomes of 900 children admitted with pneumonia, aged 2–59 months. Their study identified a limited set of protein combinations that could accurately predict which patients would later experience clinical deterioration [53]. More innovative studies developed nanosensors that respond to host protease activity and used machine learning algorithms to interpret the urine signals generated.
They found significant differences in urine reporter gene concentrations between mice with bacterial and viral pneumonia. Three (BV13, BV19, BV20) were significantly enriched in viral mice, while the other two (BV03 and BV04) were enriched in bacterial mice, demonstrating the great potential of noninvasive diagnostic techniques [54].
CHALLENGES AND FUTURE DIRECTIONS
While machine learning techniques show significant potential for diagnosing and predicting pediatric bacterial pneumonia, their widespread clinical use faces several limitations. The “black box” nature of many algorithms, especially in deep learning, raises concerns about interpretability and undermines clinical trust [55]. To address this, researchers have developed methods such as Shapley Additive exPlanations (SHAP).
These tools quantify how individual variables influence predictions, which clarifies model mechanisms and improves transparency [56]. Another major challenge is overfitting, where models perform well on training data but fail to generalize to external validation cohorts [57]. Additionally, building robust models depends heavily on both the quality and quantity of available data.
This process demands substantial domain expertise and computational resources. Importantly, incomplete or biased datasets can severely limit algorithmic performance [58].
Future research in machine learning for pediatric bacterial pneumonia should focus on three key directions. First, researchers should leverage large-scale, multicenter, and real-world datasets to reduce overfitting and improve model generalizability. Second, integrating multimodal data can enhance predictive performance.
Such data may include genomic, phenotypic, clinical, and epidemiological information. Finally, closer collaboration between technology developers and pediatricians is essential. This will help ensure that model outputs are interpretable and actionable in clinical workflows, thereby directly supporting diagnostic and treatment decisions.
CONCLUSION
This review explores the application of artificial intelligence in the early diagnosis and prediction of pediatric bacterial pneumonia. It specifically covers automated diagnosis, predictive modeling, and biomarker discovery. These technologies help clinicians utilize large-scale, multisource data for the early detection of this disease.
Furthermore, artificial intelligence supports the early identification of severe cases in clinical practice, which may lead to better patient outcomes. However, several key challenges remain. These include data heterogeneity, data quality limitations, and the black box nature of machine learning.
Addressing these issues requires collaborative and multidisciplinary approaches. As high-quality, multicenter datasets continue to grow and model interpretability improves, artificial intelligence is expected to become increasingly important in managing pediatric bacterial pneumonia.
Acknowledgements
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Conflicts of interest
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