用于识别认知障碍的自动移动认知测试：横断面可行性和诊断研究

Mayo Clinic Proceedings. Digital health Pub Date : 2025-07-16 DOI:10.1016/j.mcpdig.2025.100252

Louis Y. Tee MD, PhD , Li Feng Tan MBBS , Santhosh Seetharaman MBBS , Lian Leng Low MBBS , Zhi Peng Ong BS , Munirah Bashil BS , Hock Hai Teo PhD

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引用次数: 0

摘要

目的开发一款免费、自动化、多语言、基于人工智能的认知测试应用程序——数字处理速度测试（DPST），旨在通过利用移动医疗提高认知测试的可及性，提高对服务不足社区认知障碍的识别。在这项横断面可行性和诊断性研究中，我们比较了DPST在诊断轻度认知障碍（MCI）和痴呆方面的测试性能，并与传统的认知测试（如迷你精神状态检查（MMSE）和蒙特利尔认知评估（MoCA））进行了比较。该研究于2021年1月19日至2023年11月12日进行。通过在初级和二级保健诊所候诊区连续抽样，共招募了476名成年参与者。参与者在训练有素的评估人员的帮助下完成MMSE和MoCA，然后在移动设备上独立执行DPST。参考标准是由不知道DPST评分的记忆专家进行的MCI/痴呆的临床诊断。结果受试者工作特征曲线下面积分析显示，3个试验的曲线下面积相似(MMSE, 0.862；加州0.888;DPST, 0.861)。同样，灵敏度(DPST, 85.2%；患者,85.2%;MoCA, 90.2%)，负似然比(DPST, 0.197；MMSE, 0.193;MoCA, 0.129)，特异性(DPST, 75.0%；患者,76.5%;MoCA, 76.2%)，阳性似然比(DPST, 3.41；MMSE, 3.62;MoCA, 3.79)相似。结论数字处理速度测试是一种在移动设备上进行的免费、自动化的多语言认知测试，其测试性能与MMSE和MoCA相似。尽管如此，DPST并没有捕捉到MCI/痴呆特征的多领域认知缺陷。此外，基于人工智能的手写识别的重测信度和解释器一致性有待进一步验证。

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An Automated Mobile Cognitive Test for the Identification of Cognitive Impairment: A Cross-sectional Feasibility and Diagnostic Study

Objective

To develop Digital Processing Speed Test (DPST), a free, automated, multilingual, artificial intelligence–based cognitive testing application, with the aim to enhance recognition of cognitive impairment in underserved communities by leveraging mobile health to improve cognitive testing’s accessibility.

Patients and Methods

In this cross-sectional feasibility and diagnostic study, we determined the test performance of DPST for the identification of mild cognitive impairment (MCI) and dementia, compared with traditional cognitive tests, such as Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). The study was conducted from January 19, 2021, to November 12, 2023. In total, 476 adult participants were recruited by consecutive sampling at waiting areas of primary and secondary care clinics. The participants completed MMSE and MoCA with trained assessors and then performed DPST independently on a mobile device. The reference standard was a clinical diagnosis of MCI/dementia by a memory specialist blinded to the DPST score.

Results

Area under the receiver operating characteristic curve analyses showed that area under the curves were similar for the 3 tests (MMSE, 0.862; MoCA, 0.888; DPST, 0.861). Likewise, sensitivity (DPST, 85.2%; MMSE, 85.2%; MoCA, 90.2%), negative likelihood ratio (DPST, 0.197; MMSE, 0.193; MoCA, 0.129), specificity (DPST, 75.0%; MMSE, 76.5%; MoCA, 76.2%), and positive likelihood ratio (DPST, 3.41; MMSE, 3.62; MoCA, 3.79) were similar.

Conclusion

Digital Processing Speed Test, a free, automated, multilingual cognitive test conducted on a mobile device, has similar test performance to MMSE and MoCA. Nonetheless, DPST does not capture the multidomain cognitive deficits that characterize MCI/dementia. Moreover, test-retest reliability and interrater agreement of artificial intelligence–based handwriting recognition needs further confirmation.

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来源期刊

Mayo Clinic Proceedings. Digital health Medicine and Dentistry (General), Health Informatics, Public Health and Health Policy

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审稿时长

47 days