Abstract Preview:
Background Microcephaly and macrocephaly, which are abnormal congenital markers, are associated withdevelopmental and neurologic deficits. Hence, there is a medically imperative need to conduct ultrasound imagingearly on. However, resource-limited countries such as Ethiopia are confronted with inadequacies such that access totrained personnel and diagnostic machines inhibits the exact and continuous diagnosis from being met.Objective This study aims to develop a fetal head abnormality detection model from ultrasound images via deeplearning.Methods 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. Severalpreprocessing 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 structuresusing ultrasound images. The measurements were classified as microcephaly, macrocephaly, or normal using WHOguidelines 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.Results 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 anaccuracy of 98% and a Dice coefficient of 0.97. Compared with industry experts, the model achieved accuracies of92.5% and 91.2% for the BPD and HC measurements, respectively.Conclusion Deep learning models can enhance prenatal diagnosis workflows, especially in resource-constrainedsettings. Future work needs to be done on optimizing model performance, trying complex models, and expandingdatasets to improve generalizability. If these technologies are adopted, they can be used in prenatal care delivery.Clinical trial number Not applicable.Keywords Microcephaly, Macrocephaly, Congenital abnormality, HC, BPD
Full Abstract:
Background Microcephaly and macrocephaly, which are abnormal congenital markers, are associated withdevelopmental and neurologic deficits. Hence, there is a medically imperative need to conduct ultrasound imagingearly on. However, resource-limited countries such as Ethiopia are confronted with inadequacies such that access totrained personnel and diagnostic machines inhibits the exact and continuous diagnosis from being met.Objective This study aims to develop a fetal head abnormality detection model from ultrasound images via deeplearning.Methods 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. Severalpreprocessing 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 structuresusing ultrasound images. The measurements were classified as microcephaly, macrocephaly, or normal using WHOguidelines 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.Results 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 anaccuracy of 98% and a Dice coefficient of 0.97. Compared with industry experts, the model achieved accuracies of92.5% and 91.2% for the BPD and HC measurements, respectively.Conclusion Deep learning models can enhance prenatal diagnosis workflows, especially in resource-constrainedsettings. Future work needs to be done on optimizing model performance, trying complex models, and expandingdatasets to improve generalizability. If these technologies are adopted, they can be used in prenatal care delivery.Clinical trial number Not applicable.Keywords Microcephaly, Macrocephaly, Congenital abnormality, HC, BPD