用AI模型精准识别脑部CT中的血管性白质病变，准确率达98.5%。
Detection of Vascular Leukoencephalopathy in CT Images
- 采用ConvNext模型对脑CT图像进行二分类，自动识别白质病变
- 在1200例患者数据上实现98.5%的诊断准确率，优于3D卷积模型
- 通过热力图揭示模型关注区域，提升可解释性，适合医学影像研究者
人工智能（AI）在医学领域应用迅速发展。本研究探讨了AI在诊断脑小血管病——白质病变（leukoencephalopathy）中的作用，该病是血管性痴呆和出血性中风的主要病因。基于约1200例患者的轴向脑部CT扫描数据，训练卷积神经网络（CNN）进行二分类。针对不同患者生理导致的扫描尺寸差异，将数据统一处理，并采用三种预处理方法提升模型性能。比较了四种网络架构：ResNet50、ResNet50 3D、ConvNext 和 Densenet。结果显示，ConvNext 模型在无任何预处理条件下达到最高准确率98.5%，优于含3D卷积的模型。通过Grad-CAM热力图分析模型决策过程，揭示其关注关键病变区域。结果表明，尤其是ConvNext架构的AI可显著提升白质病变的诊断准确性，凸显深度学习在脑部疾病诊断中的潜力。
原文摘要 · Abstract (English)
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.
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