产前酒精暴露(PAE)是指由于怀孕期间饮酒而暴露于发育中的胎儿,并可能对学习产生终身影响,行为,和健康。了解PAE对发育中的大脑的影响由于其复杂的结构和功能属性而表现出挑战。这可以通过利用机器学习(ML)和深度学习(DL)方法来解决。虽然大多数ML和DL模型都是针对以成人为中心的问题量身定制的,这项工作的重点是应用DL检测儿科人群中的PAE.这项研究整合了预先训练的简单全卷积网络(SFCN)作为一种用于提取特征的迁移学习方法,以及一种新训练的分类器,用于根据2-8岁个体的T1加权结构脑磁共振(MR)扫描来区分未暴露和PAE参与者。在训练过程中几个不同的数据集大小和增强策略中,当考虑对两个类别都有增强的平衡数据集时,分类器在测试数据上获得了88.47%的最高灵敏度和85.04%的平均准确度.此外,我们还使用Grad-CAM方法初步进行了可解释性分析,突出大脑的各个区域,如call体,小脑,pons,白质是模型决策过程中最重要的特征。尽管由于大脑的快速发展,为儿科人群构建DL模型面临挑战,运动伪影,数据不足,这项工作突出了迁移学习在数据有限的情况下的潜力。此外,这项研究强调了保持平衡的数据集对公平分类的重要性,并阐明了使用可解释性分析进行模型预测的基本原理。
Prenatal alcohol exposure (PAE) refers to the exposure of the developing fetus due to alcohol consumption during pregnancy and can have life-long consequences for learning, behavior, and health. Understanding the impact of PAE on the developing brain manifests challenges due to its complex structural and functional attributes, which can be addressed by leveraging machine learning (ML) and deep learning (DL) approaches. While most ML and DL models have been tailored for adult-centric problems, this work focuses on applying DL to detect PAE in the pediatric population. This study integrates the pre-trained simple fully convolutional network (SFCN) as a transfer learning approach for extracting features and a newly trained classifier to distinguish between unexposed and PAE participants based on T1-weighted structural brain magnetic resonance (MR) scans of individuals aged 2-8 years. Among several varying dataset sizes and augmentation strategy during training, the classifier secured the highest sensitivity of 88.47% with 85.04% average accuracy on testing data when considering a balanced dataset with augmentation for both classes. Moreover, we also preliminarily performed
explainability analysis using the Grad-CAM method, highlighting various brain regions such as corpus callosum, cerebellum, pons, and white matter as the most important features in the model\'s decision-making process. Despite the challenges of constructing DL models for pediatric populations due to the brain\'s rapid development, motion artifacts, and insufficient data, this work highlights the potential of transfer learning in situations where data is limited. Furthermore, this study underscores the importance of preserving a balanced dataset for fair classification and clarifying the rationale behind the model\'s prediction using
explainability analysis.