Abstract #0739

Automatic Assessment of Fetal Gestational Age using Bayesian Deep learning Method

Axel Largent¹, Jonathan Murnick^1,2, Yuan-Chiao Lu¹, Kushal Kapse¹, Nicole Andersen¹, Todd Richmann¹, Josepheen De Asis-Cruz¹, Jessica Quistorff¹, Catherine Lopez¹, Nickie Andescavage^1,3, and Catherine Limperopoulos^1,2,4

¹Department of Diagnostic Imaging and Radiology, Children’s National Hospital, Developing Brain Institute, Washington, DC, United States, ²Departments of Radiology and Pediatrics, George Washington University, Washington, DC, United States, ³Department of Neonatology, Children's National Hospital, Washington, DC, United States, ⁴Neurology School of Medicine and Health Sciences, George Washington University, 20010, DC, United States

Synopsis

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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