Predicting gestational age at birth in the context of preterm birth from multi-modal fetal MRI
arXiv:2606.20172v1 Announce Type: new Abstract: Preterm birth is associated with significant mortality and a risk for lifelong morbidity. The complex multifactorial aetiology hampers accurate prediction and thus optimal care. A pipeline consisting of bespoke machine learning methods for data imputation, feature selection, and regression models to predict gestational age (GA) at birth was developed and evaluated from comprehensive multi-modal morphological and functional fetal MRI data from 333 control cases and 93 preterm birth cases. The GA at birth predictions were classified into term and p
The increasing availability of multi-modal medical imaging data and advances in machine learning allow for more sophisticated predictive models in healthcare.
Accurate prediction of gestational age at birth, especially in the context of preterm birth, can significantly improve medical intervention and outcomes for infants, reducing lifelong morbidity.
The ability to predict preterm birth with higher accuracy using AI could lead to earlier and more effective clinical interventions, potentially transforming neonatal care protocols.
- · Medical AI companies
- · Healthcare providers
- · Infants at risk of preterm birth
- · Parents
Improved early detection and management of preterm birth risks.
Reduced healthcare costs associated with long-term care for preterm infants.
Ethical considerations regarding early prediction and potential interventions, including reproductive choices.
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