Monitoring fetal brain development is crucial for early diagnosis of brain malformations and other congenital disorders. Standard methods to monitor brain maturation are mainly based on subjective and time-consuming visual analysis of the progression of sulcation. Our study proposed a Bayesian deep-learning method (DLM) for automatic assessment of fetal-gestational age (GA), and accurate and efficient identification of fetuses with abnormal brain development. Our Bayesian DLM showed excellent performance in predicted GA (mean-absolute-error = 0.928 weeks) and compared favorably with other state-of-the-art methods. This method may be used in clinical practice for monitoring fetal-brain development and early diagnosis of fetal brain malformations.
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