The early differentiation of Alzheimer’s disease (AD) from frontotemporal dementia (FTD) may be difficult. The Tower of London (ToL), thought to assess executive functions such as planning and visuo-spatial working memory, could help in this purpose.Twentytwo Dementia Centers consecutively recruited patients with early FTD or AD. ToL performances of these groups were analyzed using both the conventional statistical approaches and the Artificial Neural Networks (ANNs) modelling.Ninety-four non aphasic FTD and 160 AD patients were recruited. ToL Accuracy Score (AS) significantly (p < 0.05) The use of hidden information contained in the different items of ToL and the non linear processing of the data through ANNs allows a high discrimination between FTD and AD in individual patients. However, the discriminant validity of AS checked by ROC curve analysis, yielded no significant results in terms of sensitivity and specificity (AUC 0.63). The performances of the 12 Success Subscores (SS) together with age, gender and schooling years were entered into advanced ANNs developed by Semeion Institute. The best ANNs were selected and submitted to ROC curves. The nonlinear model was able to discriminate FTD from AD with an average AUC for 7 independent trials of 0.82.The use of hidden information contained in the different items of ToL and the non linear processing of the data through ANNs allows a high discrimination between FTD and AD in individual patients.

中文翻译：

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伦敦塔测试：传统统计方法与基于人工神经网络的模型在区分额颞叶痴呆与阿尔茨海默病中的比较。

早期区分阿尔茨海默病 (AD) 与额颞叶痴呆 (FTD) 可能很困难。伦敦塔 (ToL) 被认为可以评估执行功能，如规划和视觉空间工作记忆，可以帮助实现这一目的。 22 家痴呆症中心连续招募了早期 FTD 或 AD 患者。使用传统统计方法和人工神经网络 (ANN) 建模分析这些组的 ToL 表现。招募了 94 名非失语性 FTD 和 160 名 AD 患者。ToL 准确度得分 (AS) 显着 ( p< 0.05) 使用包含在 ToL 不同项目中的隐藏信息以及通过 ANN 对数据进行非线性处理，可以对个体患者的 FTD 和 AD 进行高度区分。然而，通过 ROC 曲线分析检查的 AS 判别有效性在敏感性和特异性方面没有产生显着结果（AUC 0.63）。将 12 个成功分项 (SS) 的表现连同年龄、性别和学龄输入 Semeion Institute 开发的高级 ANN。选择最好的人工神经网络并提交给 ROC 曲线。非线性模型能够将 FTD 与 AD 区分开来，7 次独立试验的平均 AUC 为 0.82。