Department of Biopharmacy, the Ludwik Rydygier Collegium Medicum in Bydgoszcz, the Nicolaus Copernicus University in Toruń, Toruń, Poland.
Department of Normal Anatomy, the Ludwik Rydygier Collegium Medicum in Bydgoszcz, the Nicolaus Copernicus University in Toruń, Toruń, Poland.
PLoS One. 2024 Mar 13;19(3):e0299062. doi: 10.1371/journal.pone.0299062. eCollection 2024.
The present article concentrates on an innovative analysis that was performed to assess the development of the femur in human fetuses using artificial intelligence. As a prerequisite, linear dimensions, cross-sectional surface areas and volumes of the femoral shaft primary ossification center in 47 human fetuses aged 17-30 weeks, originating from spontaneous miscarriages and preterm deliveries, were evaluated with the use of advanced imaging techniques such as computed tomography and digital image analysis. In order to ensure the data representativeness and to avoid introducing any hidden structures that may exist in the data, the entire dataset was randomized and separated into three subsets: training (50% of cases), testing (25% of cases), and validation (25% of cases). Based on the collected numerical data, an artificial neural network was devised, trained, and subject to testing in order to synchronously estimate five parameters of the femoral shaft primary ossification center, thus leveraging fundamental information such as gestational age and femur length. The findings reveal the formulated multi-layer perceptron model denoted as MLP 2-3-2-5 to exhibit robust predictive efficacy, as evidenced by the linear correlation coefficient between actual values and network outputs: R = 0.955 for the training dataset, R = 0.942 for validation, and R = 0.953 for the testing dataset. The authors have cogently demonstrated that the use of an artificial neural network to assess the growing femur in the human fetus may be a valuable tool in prenatal tests, enabling medical doctors to quickly and precisely assess the development of the fetal femur and detect potential anatomical abnormalities.
本文专注于一项创新分析，该分析使用人工智能评估人类胎儿股骨的发育情况。作为前提，使用先进的成像技术，如计算机断层扫描和数字图像分析，评估了 47 名 17-30 周自然流产和早产的人类胎儿股骨骨干初级骨化中心的线性尺寸、横截面积和体积。为了确保数据的代表性并避免引入数据中可能存在的任何隐藏结构，整个数据集都进行了随机化并分为三个子集：训练（50%的病例）、测试（25%的病例）和验证（25%的病例）。基于收集的数值数据，设计、训练并测试了一个人工神经网络，以同步估计股骨骨干初级骨化中心的五个参数，从而利用诸如胎龄和股骨长度等基本信息。研究结果表明，所提出的多层感知器模型 MLP 2-3-2-5 具有稳健的预测效果，因为实际值和网络输出之间的线性相关系数为：训练数据集的 R = 0.955，验证数据集的 R = 0.942，测试数据集的 R = 0.953。作者有力地证明了使用人工神经网络评估人类胎儿生长中的股骨可能是产前测试中的一种有价值的工具，使医生能够快速准确地评估胎儿股骨的发育情况并检测潜在的解剖异常。
