Artificial intelligence (AI) has seen a significant surge in popularity, particularly in its application to medicine. This study explores AI's role in diagnosing leukoencephalopathy, a small vessel disease of the brain, and a leading cause of vascular dementia and hemorrhagic strokes. We utilized a dataset of approximately 1200 patients with axial brain CT scans to train convolutional neural networks (CNNs) for binary disease classification. Addressing the challenge of varying scan dimensions due to different patient physiologies, we processed the data to a uniform size and applied three preprocessing methods to improve model accuracy. We compared four neural network architectures: ResNet50, ResNet50 3D, ConvNext, and Densenet. The ConvNext model achieved the highest accuracy of 98.5% without any preprocessing, outperforming models with 3D convolutions. To gain insights into model decision-making, we implemented Grad-CAM heatmaps, which highlighted the focus areas of the models on the scans. Our results demonstrate that AI, particularly the ConvNext architecture, can significantly enhance diagnostic accuracy for leukoencephalopathy. This study underscores AI's potential in advancing diagnostic methodologies for brain diseases and highlights the effectiveness of CNNs in medical imaging applications.

翻译：人工智能（AI）在医学领域的应用日益普及，其受欢迎程度显著提升。本研究探讨了AI在诊断脑白质病（一种脑小血管疾病，也是血管性痴呆和出血性中风的主要原因）中的作用。我们利用约1200名患者的轴位脑部CT扫描数据集，训练了用于二元疾病分类的卷积神经网络（CNNs）。针对因患者生理差异导致的扫描尺寸不一这一挑战，我们将数据处理为统一尺寸，并应用了三种预处理方法来提高模型准确性。我们比较了四种神经网络架构：ResNet50、ResNet50 3D、ConvNext和Densenet。其中，ConvNext模型在未经任何预处理的情况下达到了98.5%的最高准确率，优于采用三维卷积的模型。为了深入理解模型的决策过程，我们实现了Grad-CAM热力图，以突出模型在扫描图像上的关注区域。我们的结果表明，AI，特别是ConvNext架构，能够显著提高脑白质病的诊断准确性。本研究强调了AI在推进脑部疾病诊断方法方面的潜力，并凸显了CNNs在医学影像应用中的有效性。