AI for Preterm Brain Injury Imaging: From Automated Analysis to Clinical Translation

Abstract

Preterm brain injury is a major cause of lifelong motor, cognitive, and behavioraldisability, yet its imaging phenotype changes rapidly with development and differs across cranialultrasound (cUS) and magnetic resonance imaging (MRI). Serial cUS is suited to bedsidesurveillance of hemorrhage and ventricular enlargement, whereas term-equivalent structural anddiffusion MRI provide more detailed assessment of tissue injury, maturation, and white matterorganization. Artificial intelligence (AI) can support image-quality assessment, lesion detection,segmentation, quantitative phenotyping, developmental-age estimation, and outcome prediction,but evidence for clinical translation remains uneven. This focused review maps imaging modalitiesand computational tasks to neonatal decisions and synthesizes studies published from 2005through 2025. Structural MRI segmentation has the most consistent technical evidence; cUSautomation, subtle-lesion detection, longitudinal multimodal prediction, and prospective clinicaluse remain less mature. Dataset scale alone is not sufficient because developmental stage,acquisition differences, nonrandom missing examinations, and delayed follow-up can influenceapparent performance. We therefore distinguish model accuracy from deployability and proposea minimum pipeline comprising input governance, infant-level external evaluation, calibrateduncertainty, safety-based abstention, human review, and post-deployment monitoring. Futureprogress depends on longitudinal multicenter cohorts, development-aware representation learning,and prospective studies that measure reporting efficiency, management changes, and safety. AIshould be judged by reproducible clinical benefit rather than internal benchmark accuracy.