AI-based Risk Stratification and Management of Preterm Infants

Preterm birth is the leading cause of neonatal mortality and morbidity worldwide.

Although recent improvements in neonatal intensive care have increased the survival rates of premature infants, increasing attention is being paid to risks of short and long-term morbidities, including development delays, respiratory problems, hearing and vision impairments, neurodevelopmental disorders.

The aetiology behind preterm morbidities is multifactorial and poorly understood. It is not known which infants are at risk of developing which disease.

Thus, there is an urgent need to develop individualized prediction tool to accurately estimate morbidity risks of the preterm infants.

The aim of my proposed project is to determine the longitudinal development of preterm infants using integrative multi-omics, including blood proteome profiling, gut microbiota, and metabolome, and relate the results to gestational age, nutrition, and neonatal morbidities including bronchopulmonary dysplasia (BPD), retinopathy of prematurity (ROP), cerebral intraventricular hemorrhage (IVH), patent ductus arteriosus (PDA), sepsis and necrotizing enterocolitis (NEC).

To this end, I will develop artificial intelligence (AI) models for risk stratification and disease management with the ultimate goal to improve the quality of neonatal and childcare of preterm infants.