Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Sleep
Fetal sleep: a cross-species review of physiology, measurement, and classification
STUDY OBJECTIVES: Fetal sleep is a vital yet underexplored aspect of prenatal neurodevelopment. Its cyclic organization reflects the maturation of central neural circuits, and disturbances in these patterns may offer some of the earliest detectable signs of neurological compromise. This is the first review to integrate more than seven decades of research into a unified, cross-species synthesis of fetal sleep. We examine: (1) Physiology and Ontogeny-comparing human fetuses with animal models; and (2) Methodological Evolution-transitioning from invasive neurophysiology to non-invasive monitoring and deep learning frameworks. METHODS: A structured narrative synthesis was guided by a systematic literature search across four databases (PubMed, Scopus, IEEE Xplore, and Google Scholar). From 2925 identified records, 169 studies involving fetal sleep-related physiology, sleep-state classification, or signal-based monitoring were included in this review. RESULTS: Across the 169 studies, fetal sleep states become clearly observable as the brain matures. In fetal sheep and baboons, organized cycling between active and quiet sleep emerges at approximately 80%-90% gestation. In humans, this differentiation occurs later, around 95% gestation, with full maturation reached near term. Despite extensive animal research, no unified, clinically validated framework exists for defining fetal sleep states, limiting translation into routine obstetric practice. CONCLUSIONS: By integrating evidence across species, methodologies, and clinical contexts, this review provides the scientific foundation for developing objective, multimodal, and non-invasive fetal sleep monitoring technologies-tools that may ultimately support earlier detection of neurological compromise and guide timely prenatal intervention.
17 Apr 2026
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Deep learning in fetal, infant, and toddler neuroimaging research.
Artificial intelligence (AI) is increasingly being integrated into everyday tasks and work environments. However, its adoption in medical image analysis has progressed more slowly due to high clinical stakes, limited availability of labeled data, and substantial variability in imaging protocols and population. These challenges are further pronounced in the field of fetal, infant, and toddler (FIT) neuroimaging, where datasets are especially scarce and subject to large amounts of anatomical variability. However, deep learning (DL), a specific method within machine learning, which is itself a subfield of AI, has emerged as a powerful framework to adapt to the challenges of medical image analysis. This review is written for the broad FIT research community, including clinicians, neuroscientists, and develop mental scientists who may not have formal training in AI. To make the material accessible, we provide a concise overview of DL concepts before reviewing a selected, and non-exhaustive, list of applications of DL in FIT neuroimaging, including structural image analysis, enhancement of data acquisition, modeling of cognitive and perceptual processes, and automated video tagging. In closing, we discuss best practices for data curation, ongoing challenges, and opportunities for future research.
4 Apr 2026
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