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首页> 外文会议>Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Classifying Mild Cognitive Impairment from Behavioral Responses in Emotional Arousal and Valence Evaluation Task – AI Approach for Early Dementia Biomarker in Aging Societies –
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Classifying Mild Cognitive Impairment from Behavioral Responses in Emotional Arousal and Valence Evaluation Task – AI Approach for Early Dementia Biomarker in Aging Societies –

机译：从情绪唤醒和化合价评估任务中的行为反应对轻度认知障碍进行分类-老年社会早期痴呆症生物标志物的AI方法-

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The presented paper discusses a practical application of machine learning (ML) in the so-called ‘AI for social good’ domain and in particular concerning the problem of a potential elderly adult dementia onset prediction. An increase in dementia cases is producing a significant medical and economic weight in many countries. Approximately 47 million older adults live with a dementia spectrum of neurocognitive disorders, according to an up-to-date statement of the World Health Organization (WHO), and this amount will triple within the next thirty years. This growing problem calls for possible application of AI-based technologies to support early diagnostics for cognitive interventions and a subsequent mental wellbeing monitoring as well as maintenance with so-called ’digital-pharma’ or ’beyond a pill’ therapeutical strategies. The paper explains our attempt and encouraging preliminary study results of behavioral responses analysis in a facial emotion implicit-short-term-memory learning and evaluation experiment. We present results of various shallow and deep learning machine learning models for digital biomarkers of dementia progress detection and monitoring. The discussed machine-learning models result in median accuracies right below a 90% benchmark using classical shallow and deep learning approaches for automatic discrimination of normal cognition versus a mild cognitive impairment (MCI). The classifier input features consist of an older adult emotional valence and arousal recognition responses, together with reaction times, as well as with self-reported university-level degree education and age, as obtained from a group of 35 older adults participating voluntarily in the reported dementia biomarker development project. The presented results showcase the inherent social benefits of artificial intelligence (AI) utilization for the elderly and establish a step forward to advance machine learning (ML) approaches for the subsequent employment of simple behavioral examination for MCI and dementia onset diagnostics.Clinical relevance— This manuscript establishes a behavioral and cognitive biomarker candidate potentially substituting a Montreal Cognitive Assessment (MoCA) evaluation without a paper and pencil test.
机译：这篇论文讨论了机器学习（ML）在所谓的“为社会带来好处的AI”领域中的实际应用，特别是关于潜在的老年成人痴呆发作预测的问题。在许多国家，痴呆症病例的增加正在产生重大的医学和经济影响。根据世界卫生组织（WHO）的最新声明，大约有4700万老年人患有神经认知障碍的痴呆症，并且这一数字在未来30年内将增加两倍。这个日益严重的问题要求可能应用基于AI的技术来支持认知干预的早期诊断，随后的心理健康监测以及所谓的“数字药物”或“超越药丸”治疗策略的维持。这篇论文解释了我们在面部情绪内隐-短期记忆学习和评估实验中尝试和鼓励进行行为反应分析的初步研究结果。我们提出了针对痴呆症进展检测和监测的数字生物标志物的各种浅层和深度学习机器学习模型的结果。所讨论的机器学习模型使用经典的浅层和深度学习方法自动区分正常认知能力和轻度认知障碍（MCI），从而使中位准确度低于90％基准。分类器的输入功能包括从35名自愿参加报告的老年人中获得的老年人的情感价和唤醒识别反应以及反应时间，以及自我报告的大学水平的学历和年龄。痴呆症生物标志物开发项目。呈现的结果展示了老年人使用人工智能（AI）的内在社会效益，并迈出了一步，进一步发展了机器学习（ML）方法，随后用于MCI和痴呆症发作诊断的简单行为检查。手稿确定了一种行为和认知生物标志物候选物，可能会取代蒙特利尔认知评估（MoCA）评估，而无需进行纸笔考试。

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