Advancing Neuroprognostication in Neonatal Encephalopathy: Unlocking the Power of Artificial Intelligence

A comprehensive review of neonatal hypoxic-ischemic encephalopathy (HIE) underscores its rising prevalence and multifaceted risk factors in the U.S. between 2010 and 2018 (Acun et al., 2022). HIE, linked to significant neonatal mortality, reflects critical issues around perinatal care and highlights the need for therapeutic interventions, such as moderate hypothermia (Azzopardi et al., 2009). Long-term implications include severe impairments, warranting predictive models for clinical outcomes (Glass et al., 2024). Moreover, advancements in artificial intelligence and machine learning in neonatal care show potential for early diagnosis and improving neurodevelopmental tracking (McAdams et al., 2022; Beam et al., 2024). Additionally, placental abnormalities have been associated with adverse developmental outcomes (Mir et al., 2015). Emphasizing early intervention strategies and advanced imaging techniques could mitigate HIE impacts, facilitating enhanced neonatal care practices globally, particularly in low-resource settings (Thayyil et al., 2021; Bellos et al., 2022).

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