Mengistu Abraham Keffale, Assaye Bayou Tilahun, Flatie Addisu Baye, Mossie Zewdie
Department of Health Informatics, College of Medicine Health Science, Debre Markos University, Debre Markos, Ethiopia.
Department of Information Technology, Institute of Technology, Debre Markos University, Debre Markos, Ethiopia.
BMC Med Imaging. 2025 May 26;25(1):183. doi: 10.1186/s12880-025-01709-x.
Microcephaly and macrocephaly, which are abnormal congenital markers, are associated with developmental and neurologic deficits. Hence, there is a medically imperative need to conduct ultrasound imaging early on. However, resource-limited countries such as Ethiopia are confronted with inadequacies such that access to trained personnel and diagnostic machines inhibits the exact and continuous diagnosis from being met.
This study aims to develop a fetal head abnormality detection model from ultrasound images via deep learning.
Data were collected from three Ethiopian healthcare facilities to increase model generalizability. The recruitment period for this study started on November 9, 2024, and ended on November 30, 2024. Several preprocessing techniques have been performed, such as augmentation, noise reduction, and normalization. SegNet, UNet, FCN, MobileNetV2, and EfficientNet-B0 were applied to segment and measure fetal head structures using ultrasound images. The measurements were classified as microcephaly, macrocephaly, or normal using WHO guidelines for gestational age, and then the model performance was compared with that of existing industry experts. The metrics used for evaluation included accuracy, precision, recall, the F1 score, and the Dice coefficient.
This study was able to demonstrate the feasibility of using SegNet for automatic segmentation, measurement of abnormalities of the fetal head, and classification of macrocephaly and microcephaly, with an accuracy of 98% and a Dice coefficient of 0.97. Compared with industry experts, the model achieved accuracies of 92.5% and 91.2% for the BPD and HC measurements, respectively.
Deep learning models can enhance prenatal diagnosis workflows, especially in resource-constrained settings. Future work needs to be done on optimizing model performance, trying complex models, and expanding datasets to improve generalizability. If these technologies are adopted, they can be used in prenatal care delivery.
Not applicable.
小头畸形和大头畸形作为异常的先天性标志，与发育和神经功能缺陷相关。因此，从医学角度迫切需要尽早进行超声成像检查。然而，像埃塞俄比亚这样资源有限的国家面临着诸多不足，例如缺乏训练有素的人员和诊断设备，这阻碍了准确且持续的诊断。
本研究旨在通过深度学习从超声图像中开发一种胎儿头部异常检测模型。
从埃塞俄比亚的三个医疗保健机构收集数据，以提高模型的通用性。本研究的招募期从2024年11月9日开始，至2024年11月30日结束。已执行了多种预处理技术，如增强、降噪和归一化。应用SegNet、UNet、FCN、MobileNetV2和EfficientNet - B0使用超声图像分割并测量胎儿头部结构。根据世界卫生组织的孕周指南将测量结果分类为小头畸形、大头畸形或正常，然后将模型性能与现有行业专家的性能进行比较。用于评估的指标包括准确率、精确率、召回率、F1分数和Dice系数。
本研究能够证明使用SegNet进行自动分割、测量胎儿头部异常以及对大头畸形和小头畸形进行分类的可行性，准确率为98%，Dice系数为0.97。与行业专家相比，该模型在双顶径（BPD）和头围（HC）测量方面的准确率分别达到了92.5%和91.2%。
深度学习模型可以改进产前诊断工作流程，尤其是在资源受限的环境中。未来需要开展工作来优化模型性能、尝试复杂模型并扩大数据集以提高通用性。如果采用这些技术，它们可用于产前护理。
不适用。
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