Hamilton D, O'Mahony D, Coffey J, Murphy J, O'Hare N, Freyne P, Walsh B, Coakley D
Department of Medical Physics, Clinical and Bio-Engineering, Armed Forces Hospital, Riyadh, Saudi Arabia.
Nucl Med Commun. 1997 Sep;18(9):805-10. doi: 10.1097/00006231-199709000-00002.
An evaluation of the performance of artificial neural networks (ANNs) for the classification of probable Alzheimer's disease (pAD) patients was undertaken using data extracted from four regions of interest constructed on single photon emission tomographic (SPET) cerebral perfusion images. Two studies using feed-forward neural networks (FFNNs) were undertaken. The first was to determine if it would be possible to classify pAD patients and normal subjects in a mixed group, comprising 29 patients diagnosed as having pAD varying in severity from mild, established dementia to moderate dementia and 10 healthy control subjects. The second was to determine if the networks generated in the first study could prospectively classify 15 additional patients with very mild or mild cognitive impairment. The results were compared to those obtained using the same data and discriminant analysis. The relative performances of the two analysis techniques were assessed on the basis of the area under receiver operating characteristics (ROC) curves. The FFNN successfully classified all datasets in the first study, achieving an area under the ROC curve of 1.00, whereas discriminant analysis achieved 0.94. When tested on data from the second group, the areas under the ROC curves varied between 0.86 and 1.00 for the FFNN, whereas that for discriminant analysis was 0.99. We conclude that FFNNs can accurately classify pAD patients with mild to moderate dementia using data obtained from SPET cerebral perfusion images.
利用从基于单光子发射断层扫描（SPET）脑灌注图像构建的四个感兴趣区域提取的数据，对人工神经网络（ANN）对可能患有阿尔茨海默病（pAD）患者进行分类的性能进行了评估。进行了两项使用前馈神经网络（FFNN）的研究。第一项研究是确定在一个混合组中对pAD患者和正常受试者进行分类是否可行，该混合组包括29名被诊断患有pAD的患者，其严重程度从轻度、已确诊的痴呆到中度痴呆不等，以及10名健康对照受试者。第二项研究是确定在第一项研究中生成的网络是否能够对另外15名患有非常轻度或轻度认知障碍的患者进行前瞻性分类。将结果与使用相同数据和判别分析获得的结果进行比较。基于受试者操作特征（ROC）曲线下的面积评估了这两种分析技术的相对性能。FFNN在第一项研究中成功地对所有数据集进行了分类，ROC曲线下的面积达到了1.00，而判别分析为0.94。当在第二组数据上进行测试时，FFNN的ROC曲线下面积在0.86至1.00之间变化，而判别分析的面积为0.99。我们得出结论，FFNN可以使用从SPET脑灌注图像获得的数据准确地对轻度至中度痴呆的pAD患者进行分类。
