White Stacy,  Shellikeri Sphoorti,  Sze Raymond

Final Pr. ID: Poster #: SCI-028

Background Leg length discrepancy studies are labor intensive, yet cognitively simple, studies that represent inefficient use of the pediatric radiologists’ time and expertise.
Objective The purpose of this study was to demonstrate that measuring and calculating leg length discrepancies do not require radiologist expertise. We hypothesized that radiology technologists could be trained to quantify leg length discrepancies and that their performance would be statistically equivalent to that of board-certified, fellowship trained pediatric radiologists. Read More

Authors:  White Stacy , Shellikeri Sphoorti , Sze Raymond

Keywords:  Technologist, Leg, Length

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