Department of Neurology, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea.
Department of Neurology, Kangwon National University Hospital, Chuncheon, Republic of Korea.
Sci Rep. 2022 Aug 30;12(1):14740. doi: 10.1038/s41598-022-18696-6.
Cortical atrophy is measured clinically according to established visual rating scales based on magnetic resonance imaging (MRI). Although brain MRI is the primary imaging marker for neurodegeneration, computed tomography (CT) is also widely used for the early detection and diagnosis of dementia. However, they are seldom investigated. Therefore, we developed a machine learning algorithm for the automatic estimation of cortical atrophy on brain CT. Brain CT images (259 Alzheimer's dementia and 55 cognitively normal subjects) were visually rated by three neurologists and used for training. We constructed an algorithm by combining the convolutional neural network and regularized logistic regression (RLR). Model performance was then compared with that of neurologists, and feature importance was measured. RLR provided fast and reliable automatic estimations of frontal atrophy (75.2% accuracy, 93.6% sensitivity, 67.2% specificity, and 0.87 area under the curve [AUC]), posterior atrophy (79.6% accuracy, 87.2% sensitivity, 75.9% specificity, and 0.88 AUC), right medial temporal atrophy (81.2% accuracy, 84.7% sensitivity, 79.6% specificity, and 0.88 AUC), and left medial temporal atrophy (77.7% accuracy, 91.1% sensitivity, 72.3% specificity, and 0.90 AUC). We concluded that RLR-based automatic estimation of brain CT provided a comprehensive rating of atrophy that can potentially support physicians in real clinical settings.
皮质萎缩是根据磁共振成像（MRI）的既定视觉评估量表在临床上进行测量的。虽然脑 MRI 是神经退行性变的主要影像学标志物，但计算机断层扫描（CT）也广泛用于痴呆的早期检测和诊断。然而，它们很少被研究。因此，我们开发了一种用于自动估计脑 CT 皮质萎缩的机器学习算法。脑 CT 图像（259 例阿尔茨海默病痴呆和 55 例认知正常受试者）由三位神经科医生进行视觉评估，并用于训练。我们通过结合卷积神经网络和正则化逻辑回归（RLR）构建了一个算法。然后将模型性能与神经科医生进行比较，并测量特征重要性。RLR 可快速可靠地自动估计额部萎缩（准确率 75.2%，敏感性 93.6%，特异性 67.2%，曲线下面积 [AUC] 为 0.87）、后部萎缩（准确率 79.6%，敏感性 87.2%，特异性 75.9%，AUC 为 0.88）、右侧内侧颞叶萎缩（准确率 81.2%，敏感性 84.7%，特异性 79.6%，AUC 为 0.88）和左侧内侧颞叶萎缩（准确率 77.7%，敏感性 91.1%，特异性 72.3%，AUC 为 0.90）。我们得出结论，基于 RLR 的脑 CT 自动估计可提供全面的萎缩评分，可能有助于医生在实际临床环境中做出决策。
