AGE-US: automated gestational age estimation based on fetal ultrasound images
This addresses the need for reliable fetal growth monitoring in resource-constrained settings where manual ultrasound measurements are variable and data is limited.
The researchers tackled the problem of automated gestational age estimation from fetal ultrasound images by developing an interpretable deep learning method that uses a novel segmentation architecture and distance maps, achieving performance comparable to state-of-the-art models while reducing complexity.
Being born small carries significant health risks, including increased neonatal mortality and a higher likelihood of future cardiac diseases. Accurate estimation of gestational age is critical for monitoring fetal growth, but traditional methods, such as estimation based on the last menstrual period, are in some situations difficult to obtain. While ultrasound-based approaches offer greater reliability, they rely on manual measurements that introduce variability. This study presents an interpretable deep learning-based method for automated gestational age calculation, leveraging a novel segmentation architecture and distance maps to overcome dataset limitations and the scarcity of segmentation masks. Our approach achieves performance comparable to state-of-the-art models while reducing complexity, making it particularly suitable for resource-constrained settings and with limited annotated data. Furthermore, our results demonstrate that the use of distance maps is particularly suitable for estimating femur endpoints.