Page M P, Howard R J, O'Brien J T, Buxton-Thomas M S, Pickering A D
Hellesdon Hospital, Norwich, United Kingdom.
J Nucl Med. 1996 Feb;37(2):195-200.
The usefulness of artificial neural networks in the classification of 99mTc-HMPAO SPECT axial brain scans was investigated in a study group of Alzheimer's disease patients and age-matched normal subjects.
The cortical circumferential profiling (CCP) technique was used to extract information regarding patterns of cortical perfusion. Traditional analysis of the CCP data, taken from slices at the level of the basal ganglia, indicated significant perfusion deficits for Alzheimer's disease patients relative to normals, particularly in the left temporo-parietal and left posterior frontal areas of the cortex. The compressed profiles were then used to train a neural-network classifier, the performance of which was compared with that of a number of more traditional statistical (discriminant function) techniques and that of two expert viewers.
The optimal classification performance of the neural network (ROC area = 0.91) was better than that of the alternative statistical techniques (max. ROC area = 0.85) and that of the expert viewers (max. ROC area = 0.79).
The CCP produces perfusion profiles which are well suited to automated classification methods, particularly those employing neural networks. The technique has the potential for wide application.
在一组阿尔茨海默病患者和年龄匹配的正常受试者中，研究了人工神经网络在99mTc-HMPAO SPECT轴位脑扫描分类中的实用性。
采用皮质圆周轮廓分析（CCP）技术提取有关皮质灌注模式的信息。对取自基底神经节水平切片的CCP数据进行传统分析，结果显示，与正常受试者相比，阿尔茨海默病患者存在明显的灌注缺陷，尤其是在皮质的左颞顶叶和左后额叶区域。然后，将压缩后的轮廓用于训练神经网络分类器，并将其性能与一些更传统的统计（判别函数）技术以及两名专家阅片者的性能进行比较。
神经网络的最佳分类性能（ROC面积 = 0.91）优于其他统计技术（最大ROC面积 = 0.85）和专家阅片者（最大ROC面积 = 0.79）。
CCP产生的灌注轮廓非常适合自动分类方法，特别是那些采用神经网络的方法。该技术具有广泛应用的潜力。
