A deep learning model for the detection of various dementia and MCI pathologies based on resting-state electroencephalography data: A retrospective multicentre study.

Dementia and mild cognitive impairment (MCI) represent significant health challenges in an aging population. As the search for noninvasive, precise and accessible diagnostic methods continues, the efficacy of electroencephalography (EEG) combined with deep convolutional neural networks (DCNNs) in varied clinical settings remains unverified, particularly for pathologies underlying MCI such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and idiopathic normal-pressure hydrocephalus (iNPH). Addressing this gap, our study evaluates the generalizability of a DCNN trained on EEG data from a single hospital (Hospital #1). For data from Hospital #1, the DCNN achieved a balanced accuracy (bACC) of 0.927 in classifying individuals as healthy (n = 69) or as having AD, DLB, or iNPH (n = 188). The model demonstrated robustness across institutions, maintaining bACCs of 0.805 for data from Hospital #2 (n = 73) and 0.920 at Hospital #3 (n = 139). Additionally, the model could differentiate AD, DLB, and iNPH cases with bACCs of 0.572 for data from Hospital #1 (n = 188), 0.619 for Hospital #2 (n = 70), and 0.508 for Hospital #3 (n = 139). Notably, it also identified MCI pathologies with a bACC of 0.715 for Hospital #1 (n = 83), despite being trained on overt dementia cases instead of MCI cases. These outcomes confirm the DCNN's adaptability and scalability, representing a significant stride toward its clinical application. Additionally, our findings suggest a potential for identifying shared EEG signatures between MCI and dementia, contributing to the field's understanding of their common pathophysiological mechanisms.
Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Takufumi Yanagisawa reports financial support was provided by Japan Agency for Medical Research and Development. Takufumi Yanagisawa reports financial support was provided by Japan Science and Technology Agency. Takufumi Yanagisawa reports financial support was provided by Japan Society for the Promotion of Science. Takufumi Yanagisawa reports financial support was provided by Council of State Secretariats for Science Technology and Innovation. Takufumi Yanagisawa reports financial support was provided by National Institute of Biomedical Innovation Health and Nutrition. Takufumi Yanagisawa reports a relationship with PGV inc that includes: consulting or advisory. Takufumi Yanagisawa has patent #Information processing device, determination method, and determination program (WO 2020/218,013 A1) pending to Osaka University. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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