AI Insight
Researchers used MRI scans of newborn brains to measure dental development in over 1,400 neonates, extracting quantitative features such as tooth volume, mineralization, and arch geometry. They derived a metric called the Tooth Age Gap (TAG) to index relative dental maturity at birth, finding it was influenced by prenatal factors including gestational age, maternal pre-pregnancy BMI, and tobacco use during pregnancy. Greater dental maturity at birth predicted earlier first tooth eruption and a higher tooth count at one year of age, demonstrating that neonatal MRI can serve as an objective measure of prenatal dental programming.
Why it matters
This approach offers a non-invasive, scalable method to assess how prenatal conditions shape early development, which could improve understanding of risk factors for childhood dental problems such as caries and contribute to preventive oral health strategies starting from birth.
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Primary tooth development is shaped by prenatal experience and has consequences for childhood caries and lifelong oral health. However, evidence linking prenatal conditions to dental development has relied largely on postnatal proxies, such as parent-recalled eruption timing, that conflate prenatal programming with postnatal exposures. Here, we directly phenotype developing dentition in vivo using routinely acquired neonatal brain MRI. Using T2-weighted imaging from the HEALthy Brain and Child Development Study, we trained a 3D U-Net on 100 semi-automatic labels and applied the model to 1,433 quality-controlled neonatal scans. Automated post-processing extracted quantitative features of tooth volume, mineralization, and arch geometry. These features were used to predict postmenstrual age at MRI and to derive a bias-corrected tooth age gap (TAG), indexing relative dental maturity at birth. The segmentation model achieved mean cross-validated Dice = 0.94. Dental features predicted postmenstrual age with R2 = 0.30 and mean absolute error = 6.7 days, outperforming standard anthropometric measures (R2 = 0.22). In adjusted models, TAG varied by infant sex and was associated with gestational age at delivery, maternal pre-pregnancy BMI, and prenatal tobacco use. In infants with longitudinal dental follow-up, greater dental maturity at birth predicted earlier first tooth emergence and more teeth at one year. Automated segmentation and age prediction generalized to an independent cohort. These findings establish neonatal dental MRI phenotyping as an objective, scalable index of dental maturation and a potential readout of prenatal influences on a developmental system relevant to lifelong oral health.