Satake Yuto, Taomoto Daiki, Wu Shuqiong, Godó Ákos, Sato Shunsuke, Suzuki Maki, Okura Fumio, Yagi Yasushi, Ikeda Manabu
Department of Psychiatry, Osaka University Graduate School of Medicine, 2-2 D3 Yamadaoka, Suita, Osaka, 565-0871, Japan.
Division of Psychiatry, University College London, 6th Floor Wings B, Maple House, 149 Tottenham Ct Rd, London, W1T 7NF, UK.
Sci Rep. 2025 Apr 22;15(1):13989. doi: 10.1038/s41598-025-98319-y.
Dual-task composed of gait or stepping tasks combined with cognitive tasks has been well-established as valuable tools for detecting neurocognitive disorders such as mild cognitive impairment and early-stage Alzheimer's disease. We previously developed a novel dual-task system with high accuracy for differentiating patients with neurocognitive disorders from healthy controls. In this study, we aimed to elucidate whether the output value obtained through artificial intelligence assumptions has clinical meaning other than diagnosis labelling. This is a retrospective cross-sectional study. Patients with Alzheimer's disease dementia, dementia with Lewy bodies, or mild cognitive impairment who participated in our previous dual-task experiment and completed all routine neuropsychological assessments at our hospital within one year of the experimental date were eligible for inclusion in the neurocognitive disorders group. Participants in the healthy control group were recruited from community-dwelling older adults. The correlation between the output value, "y-value", and each neuropsychological test: Mini-Mental State Examination (MMSE), Addenbrook's Cognitive Examination, Logical Memory tests, Frontal Assessment Battery, and digit span were assessed by Pearson's correlation coefficient. We also evaluated the correlation between the MMSE and those neurocognitive tests. To elucidate the diagnostic availability of the dual-task system and the MMSE on this dataset, we conducted a receiver operating characteristic analysis. We enrolled 97 participants in the neurocognitive disorders group: 42 with Alzheimer's disease dementia, 11 with dementia with Lewy bodies, and 44 with mild cognitive impairment. Additionally, 249 participants were included in the healthy control group. Although the y-value showed significant correlations with several tests, the MMSE demonstrated much stronger significant correlations with a broader range of cognitive tests. Meanwhile, its sensitivity and specificity were 0.969 and 0.912, respectively, and the area under the curve was 0.981, which was higher than the 0.934 of the MMSE. Our new AI-driven dual-task system has a high ability to predict neurocognitive disorders. However, the clinical significance of its output values is limited to screening for neurocognitive disorders and does not extend to estimating cognitive function. When using this system in clinical practice, it is essential to understand its limitations and select the appropriate usage scenarios.
由步态或步行任务与认知任务相结合组成的双重任务，已被公认为是检测神经认知障碍（如轻度认知障碍和早期阿尔茨海默病）的重要工具。我们之前开发了一种新型双重任务系统，该系统在区分神经认知障碍患者与健康对照方面具有很高的准确性。在本研究中，我们旨在阐明通过人工智能假设获得的输出值除诊断标签外是否具有临床意义。这是一项回顾性横断面研究。在实验日期后一年内参加过我们之前的双重任务实验并在我院完成所有常规神经心理学评估的阿尔茨海默病痴呆、路易体痴呆或轻度认知障碍患者符合纳入神经认知障碍组的条件。健康对照组的参与者从社区居住的老年人中招募。通过皮尔逊相关系数评估输出值“y值”与各项神经心理学测试：简易精神状态检查表（MMSE）、阿登布鲁克认知检查表、逻辑记忆测试、额叶评估量表和数字广度之间的相关性。我们还评估了MMSE与那些神经认知测试之间的相关性。为了阐明双重任务系统和MMSE在该数据集上的诊断可用性，我们进行了受试者工作特征分析。我们招募了97名神经认知障碍组的参与者：42名阿尔茨海默病痴呆患者、11名路易体痴呆患者和44名轻度认知障碍患者。此外，249名参与者被纳入健康对照组。尽管y值与多项测试显示出显著相关性，但MMSE与更广泛的认知测试显示出更强的显著相关性。同时，其敏感性和特异性分别为0.969和0.912，曲线下面积为0.981，高于MMSE的0.934。我们新的人工智能驱动的双重任务系统具有很高的预测神经认知障碍的能力。然而，其输出值的临床意义仅限于筛查神经认知障碍，并不扩展到估计认知功能。在临床实践中使用该系统时，必须了解其局限性并选择合适的使用场景。
