Methicillin-susceptible Staphylococcus aureus (MSSA) remains a major pathogen in neonatal intensive care units (NICUs), with colonization and infection posing significant risks. MSSA colonization occurs in up to 42.8% of neonates, while 12–41% of healthcare personnel also carry MSSA, contributing to nosocomial transmission. MSSA accounts for approximately 12% of neonatal S. aureus bloodstream infections, with mortality rates up to 20.5%, particularly among very-low-birth-weight infants. This review analyzes the molecular attributes, epidemiology, risk factors, clinical presentations, decontamination methods, and treatment alternatives for MSSA infections in newborns. MSSA strains show considerable genetic heterogeneity, being distinguished by a wide variety of sequence types (STs) and Staphylococcal Protein A types (SpA). They harbor several pathogenicity genes—including hemolysins, superantigens, adhesins, and Panton–Valentine leukocidin (PVL)—which are implicated in severe infections, while biofilm-associated genes enhance environmental persistence. Prematurity, low birth weight, prolonged hospitalization, and exposure to invasive devices are key risk factors. Active surveillance and decolonization programs have achieved reductions of up to 73% in MSSA infections. β-lactam antibiotics remain first-line therapy, with adjunctive aminoglycosides reserved for severe cases. Ongoing genomic surveillance and targeted preventive strategies are essential to reduce MSSA-associated morbidity and mortality in this vulnerable population.

Respiratory disorders remain a major cause of morbidity and mortality in neonatal intensive care units, particularly among preterm infants. Advances in physiological monitoring, medical imaging, and electronic health records have enabled the growing application of artificial intelligence in neonatal respiratory care. This narrative review summarizes current applications and emerging directions of artificial intelligence in the diagnosis, monitoring, and management of neonatal respiratory disorders. Machine learning and deep learning approaches have demonstrated promising performance in respiratory distress syndrome, bronchopulmonary dysplasia, apnea of prematurity, ventilatory management, and severe respiratory complications. By integrating multimodal clinical, physiological, and imaging data, these methods support earlier detection of respiratory deterioration and improved clinical decision-making. However, challenges related to data quality, generalizability, interpretability, and limited prospective validation continue to constrain widespread clinical implementation, highlighting the need for careful integration into neonatal care workflows.

Neonatal echocardiography remains an essential imaging modality for the assessment of congenital and hemodynamic cardiovascular abnormalities in critically ill neonates. Recent advances in quantitative ultrasound biomarkers, deformation imaging, volumetric reconstruction, and artificial intelligence (AI)-assisted analysis have substantially expanded the diagnostic capabilities of neonatal cardiovascular ultrasound imaging. This narrative review critically examines current developments in quantitative echocardiographic imaging, advanced volumetric methodologies, and AI-assisted cardiovascular ultrasound analysis, with emphasis on neonatal intensive care applications. A literature search was conducted using PubMed, Google Scholar, Scopus and Embase focusing on neonatal echocardiography, spatiotemporal image correlation (STIC), speckle-tracking echocardiography, artificial intelligence, machine learning, and quantitative cardiovascular imaging. Recent studies suggest that advanced methodologies, including speckle-tracking echocardiography, STIC-based reconstruction, automated segmentation algorithms, and deep learning frameworks, may improve image standardization, automated quantification, and congenital heart disease detection. However, important challenges persist, including operator dependency, dataset heterogeneity, limited external validation, cross-platform variability, and incomplete integration into routine neonatal intensive care workflows. Most AI-assisted echocardiographic systems remain investigational and require further prospective multicenter validation before widespread clinical implementation can be achieved.

Neonatal sepsis remains a major cause of morbidity and mortality worldwide, while timely diagnosis continues to be challenging because of nonspecific clinical manifestations and limitations of conventional diagnostic methods. Recent advances in artificial intelligence (AI) have created new opportunities for the early prediction and diagnosis of neonatal sepsis through the analysis of large and complex clinical datasets. This structured narrative review summarizes current evidence regarding AI-based approaches for neonatal sepsis prediction and diagnosis. A literature search of PubMed, Scopus, and Google Scholar identified studies evaluating machine learning, deep learning, and advanced predictive analytics using clinical, laboratory, physiological, electronic health record, and multi-omics data. Current evidence demonstrates that AI models, particularly ensemble learning, gradient boosting, and deep learning approaches, can achieve strong predictive performance and identify infants at increased risk of sepsis hours before conventional clinical recognition. Continuous physiological monitoring and multimodal data integration appear particularly promising for real-time prediction. However, important challenges remain, including limited external validation, small and heterogeneous datasets, concerns regarding interpretability, and unresolved ethical and regulatory issues. Future progress will depend on multicenter collaboration, explainable AI frameworks, federated learning, and multimodal predictive models. Artificial intelligence has the potential to become a valuable decision-support tool that enhances early sepsis recognition and supports precision neonatal care.

Methicillin-sensitive Staphylococcus aureus (MSSA) infection continues to be a significant public health issue in neonatal intensive care units (NICUs). Although the research mostly focuses on methicillin-resistant Staphylococcus aureus (MRSA), new data reveal that methicillin-sensitive Staphylococcus aureus (MSSA) infections are three to four times more prevalent in neonates, correlating with increased mortality and morbidity. This study analyzes the molecular attributes, epidemiology, risk factors, clinical presentations, decontamination methods, and treatment alternatives for MSSA infections in newborns. Results demonstrate that MSSA strains possess considerable genetic diversity, as they are distinguished by a wide variety of sequence types (STs) and Staphylococcal Protein A types (SpA) and harbor several pathogenicity genes, including hemolysins and superantigens, which are implicated in severe infections, such as necrotizing pneumonia in PVL-positive strains. The epidemiological load is still high over the globe, and more newborns, parents, and healthcare workers are being colonized. Prematurity, low birth weight, lengthy hospitalization and the use of intrusive technologies are some of the biggest risk factors. Clinical signs include skin and soft tissue infections, bacteremia, and sepsis, with severe consequences such as endocarditis. Decolonization procedures using mupirocin and chlorhexidine have shown efficacy, but constrained by escalating resistance. In terms of treatment, β-lactams are still the best option. Cefazolin and isoxazolylpenicillins work better than vancomycin in clinical results. For a limited time, they may also be used with aminoglycosides for severe infections. In conclusion, MSSA infections in newborns are as perilous as MRSA, necessitating enhanced monitoring, the implementation of focused prevention strategies, and the development of novel treatment methods to mitigate morbidity and death in this susceptible population.

Neonatal sepsis remains a major cause of morbidity and mortality worldwide, while timely diagnosis continues to be challenging because of nonspecific clinical manifestations and limitations of conventional diagnostic methods. Recent advances in artificial intelligence (AI) have created new opportunities for the early prediction and diagnosis of neonatal sepsis through the analysis of large and complex clinical datasets. This structured narrative review summarizes current evidence regarding AI-based approaches for neonatal sepsis prediction and diagnosis. A literature search of PubMed, Scopus, and Google Scholar identified studies evaluating machine learning, deep learning, and advanced predictive analytics using clinical, laboratory, physiological, electronic health record, and multi-omics data. Current evidence suggests that AI models, particularly ensemble learning, gradient boosting, and deep learning approaches, can achieve promising predictive performance and identify infants at increased risk of sepsis hours before conventional clinical recognition. Continuous physiological monitoring and multimodal data integration appear particularly promising for real-time prediction. However, important challenges remain, including limited external validation, small and heterogeneous datasets, concerns regarding interpretability, and unresolved ethical and regulatory issues. Future progress will depend on multicenter collaboration, explainable AI frameworks, federated learning, and multimodal predictive models. Although current evidence supports the predictive potential of AI-based models, prospective multicenter validation and clinical impact studies are required before improvements in neonatal clinical outcomes can be established. Artificial intelligence has the potential to become a valuable clinical decision support tool to support early sepsis recognition and precision neonatal care.