Brain age is an emerging concept that reflects complex, time‐dependent changes in brain structure, identifying departures from expected neurodevelopmental patterns. In the developing brain, accurate MRI‐based age estimation is a quantitative biomarker for detecting atypical neurodevelopment, facilitating early diagnosis, guiding clinical decision‐making, and potentially improving long‐term outcomes. Data‐driven models applied to neuroimaging have provided valuable insights into the pathogenesis of various congenital and acquired pediatric conditions. In particular, advanced deep learning approaches have recently gained prominence in a wide range of pediatric neuroimaging studies, offering state‐of‐the‐art performance in estimating developmental brain age. In this survey, we provide a comprehensive review of the current MRI applications of deep learning methodologies for developmental brain age (fetal stage—2 years) estimation. We provide details on both clinical and technical aspects, open‐access developmental MRI datasets, and compare the performance of these models utilizing evaluation metrics. Additionally, we discuss the applications of brain age estimation in clinical research contexts, highlighting its importance in understanding neurodevelopmental disorders. Finally, we address the challenges faced and propose future research directions to advance the field of brain age estimation. We aim to provide valuable insights for researchers and practitioners, facilitating advancements in both theoretical understanding and practical applications of MRI‐based deep learning brain age estimation of the developing brain. Evidence Level: 3. Technical Efficacy: Stage 2.
Asma-ull et al. (Fri,) studied this question.