Neuro-multimodal monitoring in neonatal brain injury: Current applications, challenges, and future directions

Advances in neuromultimodal monitoring (MMM) have significantly enhanced early detection and prognostic evaluation of brain injury in neonatal intensive care. This review summarizes the application and integration of multimodal monitoring technologies for brain injury assessment in newborns. Compared to single-modality approaches, MMM-based combined monitoring strategies demonstrate superior accuracy in both injury identification and prediction of neurodevelopmental outcomes. Meanwhile, artificial intelligence (AI) methods are increasingly being utilized for multi-source data integration and modeling, showing considerable potential in identifying subtle biomarkers and optimizing risk stratification.However, several challenges-including insufficient standardization of monitoring parameters, lack of device calibration protocols, limited multicenter data sharing, and the "black-box" nature and poor clinical interpretability of AI algorithms-hinder the clinical translation and broad application of MMM technologies. Looking forward, further development of MMM requires the establishment of unified standardized frameworks and open-access databases, alongside enhanced validation of AI-assisted algorithms for transparency and reliability, and the development of more efficient data integration models. These advances are essential to solidify the core role of MMM in neonatal neuromonitoring systems.