MACS Laboratory, National Engineering School of Gabes, University of Gabes, 6029, Gabès, Tunisia.
Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, 61421, Abha, Saudi Arabia.
Sci Rep. 2023 Oct 20;13(1):17904. doi: 10.1038/s41598-023-44689-0.
Ultrasound imaging is commonly used to aid in fetal development. It has the advantage of being real-time, low-cost, non-invasive, and easy to use. However, fetal organ detection is a challenging task for obstetricians, it depends on several factors, such as the position of the fetus, the habitus of the mother, and the imaging technique. In addition, image interpretation must be performed by a trained healthcare professional who can take into account all relevant clinical factors. Artificial intelligence is playing an increasingly important role in medical imaging and can help solve many of the challenges associated with fetal organ classification. In this paper, we propose a deep-learning model for automating fetal organ classification from ultrasound images. We trained and tested the model on a dataset of fetal ultrasound images, including two datasets from different regions, and recorded them with different machines to ensure the effective detection of fetal organs. We performed a training process on a labeled dataset with annotations for fetal organs such as the brain, abdomen, femur, and thorax, as well as the maternal cervical part. The model was trained to detect these organs from fetal ultrasound images using a deep convolutional neural network architecture. Following the training process, the model, DenseNet169, was assessed on a separate test dataset. The results were promising, with an accuracy of 99.84%, which is an impressive result. The F1 score was 99.84% and the AUC was 98.95%. Our study showed that the proposed model outperformed traditional methods that relied on the manual interpretation of ultrasound images by experienced clinicians. In addition, it also outperformed other deep learning-based methods that used different network architectures and training strategies. This study may contribute to the development of more accessible and effective maternal health services around the world and improve the health status of mothers and their newborns worldwide.
超声成像是一种常用于辅助胎儿发育的技术。它具有实时性、低成本、非侵入性和易于使用的优点。然而，胎儿器官检测对于妇产科医生来说是一项具有挑战性的任务，它取决于几个因素，例如胎儿的位置、母亲的体型和成像技术。此外，图像解释必须由经过培训的医疗保健专业人员进行，他们能够考虑所有相关的临床因素。人工智能在医学成像中发挥着越来越重要的作用，可以帮助解决与胎儿器官分类相关的许多挑战。在本文中，我们提出了一种从超声图像中自动分类胎儿器官的深度学习模型。我们在包含来自不同地区的两个数据集的胎儿超声图像数据集上训练和测试了该模型，并使用不同的机器记录它们，以确保有效检测胎儿器官。我们在带有胎儿器官（如脑、腹部、股骨和胸部）以及母体宫颈部分的标注数据集上进行了训练过程。该模型使用深度卷积神经网络架构来从胎儿超声图像中检测这些器官。在训练过程之后，使用 DenseNet169 对单独的测试数据集进行了评估。结果令人鼓舞，准确率为 99.84%，这是一个令人印象深刻的结果。F1 得分为 99.84%，AUC 为 98.95%。我们的研究表明，所提出的模型优于依赖经验丰富的临床医生手动解释超声图像的传统方法。此外，它还优于使用不同网络架构和训练策略的其他基于深度学习的方法。这项研究可能有助于在全球范围内开发更便捷和有效的孕产妇保健服务，并改善全球母亲及其新生儿的健康状况。
