Clinical procedure for mild cognitive impairment (MCI) is mainly based on clinical records and short cognitive tests. However, low suspicion and difficulties in understanding test cut-offs make diagnostic accuracy being low, particularly in primary care. Artificial neural networks (ANNs) are suitable to design computed aided diagnostic systems because of their features of generating relationships between variables and their learning capability. The main aim pursued in that work is to explore the ability of a hybrid ANN-based system in order to provide a tool to assist in the clinical decision-making that facilitates a reliable MCI estimate. The model is designed to work with variables usually available in primary care, including Minimental Status Examination (MMSE), Functional Assessment Questionnaire (FAQ), Geriatric Depression Scale (GDS), age, and years of education. It will be useful in any clinical setting. Other important goal of our study is to compare the diagnostic rendering of ANN-based system and clinical physicians. A sample of 128 MCI subjects and 203 controls was selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The ANN-based system found the optimal variable combination, being AUC, sensitivity, specificity, and clinical utility index (CUI) calculated. The ANN results were compared with those from medical experts which include two family physicians, a neurologist, and a geriatrician. The optimal ANN model reached an AUC of 95.2%, with a sensitivity of 90.0% and a specificity of 84.78% and was based on MMSE, FAQ, and age inputs. As a whole, physician performance achieved a sensitivity of 46.66% and a specificity of 91.3%. CUIs were also better for the ANN model. The proposed ANN system reaches excellent diagnostic accuracy although it is based only on common clinical tests. These results suggest that the system is especially suitable for primary care implementation, aiding physicians work with cognitive impairment suspicions.

中文翻译：

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基于混合神经架构的用于轻度认知障碍检测的实时临床决策支持系统

轻度认知障碍 (MCI) 的临床程序主要基于临床记录和简短的认知测试。然而，低怀疑和难以理解检测临界值使得诊断准确性低，特别是在初级保健中。人工神经网络 (ANN) 适合设计计算机辅助诊断系统，因为它们具有在变量之间生成关系的特性及其学习能力。该工作的主要目标是探索基于混合 ANN 的系统的能力，以提供一种工具来协助临床决策，从而促进可靠的 MCI 估计。该模型旨在处理初级保健中通常可用的变量，包括最低精神状态检查 (MMSE)、功能评估问卷 (FAQ)、老年抑郁量表 (GDS)、年龄、和多年的教育。它将在任何临床环境中有用。我们研究的另一个重要目标是比较基于 ANN 的系统和临床医生的诊断结果。从阿尔茨海默病神经影像学倡议 (ADNI) 中选择了 128 名 MCI 受试者和 203 名对照的样本。基于 ANN 的系统找到了最佳变量组合，即 AUC、敏感性、特异性和计算的临床效用指数 (CUI)。ANN 结果与医学专家的结果进行了比较，医学专家包括两名家庭医生、一名神经科医生和一名老年科医生。最优 ANN 模型的 AUC 达到 95.2%，敏感性为 90.0%，特异性为 84.78%，基于 MMSE、FAQ 和年龄输入。总体而言，医生表现的敏感性为 46.66%，特异性为 91.3%。CUI 对于 ANN 模型也更好。尽管所提出的 ANN 系统仅基于常见的临床测试，但它仍能达到极好的诊断准确性。这些结果表明该系统特别适用于初级保健实施，帮助医生处理怀疑有认知障碍的工作。