PARENT activated 15 PhD research projects, each primarily aligned with one of the five Specific Objectives (SOs), although several ESRs contributed across multiple areas. Below is a summary of their main work, categorized by the SO most directly addressed.
SO1 – Neonatal Brain Specific Hybrid Neuroimaging Technology
• ESR3 studied neurodevelopmental trajectories using neuroimaging and electric signals. Ethical protocols were submitted, and clinical studies were launched to explore brain maturation, injury markers, and outcome correlations.
• ESR6 developed deep learning methods for the segmentation of neonatal brain structures in MRI and 2D/3D ultrasound, leveraging radiomic features for early risk stratification.
• ESR11 built a deep learning framework to quantify anatomical structures across age ranges, facilitating comparison between neonatal and later stages of development.
• ESR15 focused on integrating CNNs trained on infant MRIs to identify early “fingerprints” of neurodevelopmental disorders. Latest deliverables confirm strong predictive performance in cross-site validation.
SO2 – Personalized Eye Tracking in Newborn at Neurological Risk
• ESR8 designed and optimized ML workflows for analyzing eye-tracking data from infants aged 3–24 months, producing an efficient early diagnostic tool.
• ESR10 developed a battery of computerized neuropsychological tests based on eye-tracking to detect attention, visuomotor and cognitive anomalies in preterms.
• ESR12 studied visual and oculomotor deficits in children with unilateral cerebral palsy. Clinical data collection is now complete and retrospective analyses are ongoing.
SO3 – Congenital Heart Disease and Neurodevelopmental Diseases Relationships
• ESR13 investigated neurological biomarkers in infants with congenital heart disease, with specific focus on those with Fontan circulation. Clinical protocols and preliminary datasets have been finalized.
• ESR2 developed AI-supported tools for automated ECG reading and 3D ultrasound alignment, enabling prediction of neurological outcomes in infants with cardiac anomalies.
SO4 – Computational Modelling to Predict ncRNA–NDD Association
• ESR1 developed a hybrid multi-objective evolutionary platform integrating XGBoost classifiers for biomarker discovery. The method was validated on several transcriptomic datasets.
• ESR4 established experimental protocols to study miRNA alterations in preterm neonates, including extraction pipelines and correlations with MRI data.
SO5 – Multidimensional Landscape Characterizing Neurodevelopmental Diseases
• ESR5 built the Neonates Recording Platform (NRP): a heterogeneous multi-source data acquisition system for NICUs, allowing real-time collection of physiological and behavioral parameters.
• ESR9 applied ML to emotional voice recognition and behavior modeling, extracting features predictive of risk in early infancy.
• ESR7 integrated multimodal data and trained classifiers to predict abnormal MRI results at 2 years in preterms.
• ESR14 designed an AI-based software architecture integrating semantic modeling and decision-support tools to analyze heterogeneous data for trajectory prediction.
