ADVANCES IN ARTIFICIAL INTELLIGENCE AND DEEP LEARNING FOR NEUROSONOGRAPHIC AND CHEST X-RAY ANALYSIS OF PREMATURE INFANTS

Abstract

Premature infants represent one of the most vulnerable patient populations in neonatology, frequently affected by severe neurological and respiratory complications that require rapid, reliable, and accurate diagnosis. Conditions such as hypoxic-ischemic encephalopathy (HIE) and respiratory distress syndrome (RDS) are among the leading causes of morbidity and mortality in premature newborns, often associated with long-term developmental and health consequences. Early detection and continuous monitoring of these conditions are essential for improving therapeutic outcomes and reducing the risk of severe complications. In recent years, advances in artificial intelligence (AI), deep learning, and computer-assisted medical imaging have opened new possibilities for improving diagnostic precision, reducing subjectivity in image interpretation, and supporting clinical decision-making in neonatal intensive care units (NICUs). This paper presents an overview of contemporary AI-based approaches in neonatal imaging, with a particular focus on neurosonographic analysis of HIE and chest X-ray analysis of RDS in premature infants. The presented methodologies include convolutional neural network (CNN)-based classification of neurosonographic findings using echogenicity analysis and Delta E CIE76 quantification, as well as computer-assisted lung segmentation and radiographic analysis combined with blood gas parameters‘ evaluation. The neurosonographic classification model demonstrated high performance in differentiating normal, moderate, and severe pathological findings, while lung segmentation algorithms achieved promising accuracy and robustness in monitoring respiratory recovery and evaluating disease progression. In addition, the integration of quantitative image analysis with clinical parameters enables more objective assessment of neonatal conditions and supports the development of intelligent diagnostic support systems. By combining medical image processing, machine learning, and clinical data analysis, these approaches demonstrate the considerable potential of AI technologies for early diagnosis, treatment planning, and continuous monitoring of premature newborns. Furthermore, the study highlights the growing importance of intelligent diagnostic systems in modern neonatology and emphasizes the future potential of explainable and multimodal AI models for improving neonatal healthcare outcomes, optimizing therapeutic strategies, and advancing personalized neonatal care