Development of a Deep-Learning Model for Estimating Newborn Gestational Age via Lumbar Vertebral Segmentation on Plain Radiography.

OBJECTIVE

To develop a deep learning model for estimating newborn gestational age (GA) based on the shape of the lumbar vertebral bodies on cross-table lateral radiographs obtained on the first day after birth.

MATERIALS AND METHODS

This retrospective study included 423 cross-table lateral radiographs of 423 newborns (242 boys and 181 girls) taken within 24 hours after birth at two hospitals. Of these, 256 radiographs (157 boys and 99 girls) obtained from one institution were used for model development, and 167 radiographs (85 boys and 82 girls) from the other institution were used for model external testing. Clinical data, including medical history of underlying disorders, GA determined by ultrasound parameters, birth date, birth weight, sex, examination date, and reason for requesting radiographs, were obtained. The radiographs underwent manual labeling of the five lumbar vertebral bodies, followed by preprocessing steps such as normalization, resizing, denoising, cropping, and augmentation via horizontal flipping and rotation. Subsequently, we trained a deep learning model using a DeepLabv3+ network with a ResNet50 backbone for lumbar segmentation and used a customized AgeClassifier model with two parallel ResNet18 backbones for GA estimation. Model performance was evaluated using an external test dataset after image cropping.

RESULTS

Neither GA nor birth weight differed significantly between boys and girls. In the segmentation model, the mean dice similarity coefficient ± standard deviation (SD) was 0.801 ± 0.031. For GA estimation, the mean absolute error ± SD was 5.2 ± 0.5 days. The Bland-Altman bias (AI-estimated GA - ground truth GA) and 95% limits of agreement were -0.4 days and -13.0 to 12.3 days, respectively.

CONCLUSION

Our deep learning model showed promising performance in lumbar vertebral body segmentation and GA estimation using plain radiographs, suggesting its potential utility as a supportive tool for neonatal maturity assessment in clinical practice.