Bedoya M. Alejandra,  Iwasaka-neder Jade,  Bixby Sarah,  Tsai Andy

Final Pr. ID: Poster #: SCI-005

Knowing the gestational status of a neonate (premature or full-term) impacts a pediatric radiologist’s ability to generate a reasonable differential diagnosis of neonatal diseases. Though crucial, this information is often unavailable at the time of the babygram interpretation. Conventional methods based on measuring the clavicular-pubic length (CPL) and the transverse diameter of the chest (TDC) from a babygram have been proposed as reliable estimators of a neonate’s gestational age. In this study, we aimed to compare these two conventional methodologies to that of a deep-learning (DL) model in determining a neonate’s gestational status. Read More

Authors:  Bedoya M. Alejandra , Iwasaka-neder Jade , Bixby Sarah , Tsai Andy

Keywords:  Artificial intelligence, Clavicular-pubic length, Chest width