Ham Sungwon, Choi Gayoung, Je Bo-Kyung, Oh Saelin
Healthcare Readiness Institute for Unified Korea, Korea University Ansan Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
Korean J Radiol. 2025 Sep;26(9):867-876. doi: 10.3348/kjr.2025.0172.
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.
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.
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.
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.
基于出生后第一天获得的交叉台面侧位X线片上腰椎椎体的形状，开发一种用于估计新生儿胎龄（GA）的深度学习模型。
这项回顾性研究纳入了两家医院在出生后24小时内拍摄的423例新生儿（242例男孩和181例女孩）的423张交叉台面侧位X线片。其中，从一个机构获得的256张X线片（157例男孩和99例女孩）用于模型开发，从另一个机构获得的167张X线片（85例男孩和82例女孩）用于模型外部测试。获取了临床数据，包括潜在疾病病史、通过超声参数确定的GA、出生日期、出生体重、性别、检查日期以及要求拍摄X线片的原因。对X线片进行了五个腰椎椎体的手动标记，随后进行了诸如归一化、调整大小、去噪、裁剪以及通过水平翻转和旋转进行增强等预处理步骤。随后，我们使用具有ResNet50主干的DeepLabv3+网络训练了一个用于腰椎分割的深度学习模型，并使用具有两个并行ResNet18主干的定制AgeClassifier模型进行GA估计。在图像裁剪后，使用外部测试数据集评估模型性能。
男孩和女孩之间的GA和出生体重均无显著差异。在分割模型中，平均骰子相似系数±标准差（SD）为0.801±0.031。对于GA估计，平均绝对误差±SD为5.2±0.5天。Bland-Altman偏差（AI估计的GA - 真实GA）和95%一致性界限分别为-0.4天和-13.0至12.3天。
我们的深度学习模型在使用平片进行腰椎椎体分割和GA估计方面表现出了有前景的性能，表明其在临床实践中作为新生儿成熟度评估辅助工具的潜在效用。
