Artificial Intelligence for Neonatal Cerebral Atrophy: A Systematic Review of Deep Learning, Explainable AI, and State-Space Models

Neonatal cerebral atrophy is a serious form of structural brain anomaly observed in up to 15% of newborns admitted to neonatal intensive care units (NICUs). Early and accurate assessment is clinically important because it can inform prognosis, guide neuroprotective and rehabilitative management, and support long-term developmental monitoring. This systematic review, conducted and reported in accordance with the PRISMA 2020 statement, searched four electronic databases (PubMed/MEDLINE, Scopus, Web of Science, and IEEE Xplore) from inception to August 2026, yielding 312 records; after two-stage screening by two independent reviewers, 90 peer-reviewed studies were included and assessed for reporting quality using the CLAIM checklist. The review found that deep learning methods for neonatal brain MRI are advancing rapidly in tissue segmentation, brain age estimation, injury detection, and anomaly detection; however, no included study directly addressed the diagnosis of neonatal cerebral atrophy, and no validated atrophy-specific model, dataset, or benchmark currently exists. Transferable methodological foundations were identified in convolutional and U-Net architectures, Transformers, and Mamba-based state-space models, the latter offering linear-complexity long-range modeling that is potentially advantageous for high-resolution 3D neonatal MRI; these architectures are presented as promising research options that still require direct comparative validation. Publicly available neonatal MRI datasets lack radiologist-confirmed atrophy labels, and explainable AI methods remain largely unvalidated in neonatal neuroimaging. The review concludes by defining priority directions: neonatal-specific 3D architectures, age-normalized volumetric biomarkers, externally validated explainable pipelines, and curated atrophy-labeled datasets.