Machine learning model for predicting the conversion to dementia using the Cube Copying Test

Shinozaki, Mio; Hishida, Hiroyuki; Gondo, Yasuyuki; Yamamoto, Michio; Suzuki, Takashi; Miura, Rina; Sakurai, Takashi; Takeda, Akinori; Arahata, Yutaka

doi:https://doi.org/10.1177/13872877251376939

Machine learning model for predicting the conversion to dementia using the Cube Copying Test

Shinozaki, Mio; Hishida, Hiroyuki; Gondo, Yasuyuki et al.

Journal of Alzheimer’s Disease, 2025, 108(1_suppl.), 141-158

Number of Access:329（2026-02-18 20:07 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/102844

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Title	Machine learning model for predicting the conversion to dementia using the Cube Copying Test
Creator	NRID 1000030813828 1000030813828 ORCID 0000-0002-3103-7004 0000-0002-3103-7004 CreatorName Shinozaki, Mio
	ORCID 0009-0000-4025-0088 0009-0000-4025-0088 CreatorName Hishida, Hiroyuki
	NRID 1000040250196 1000040250196 ORCID 0000-0002-9805-2807 0000-0002-9805-2807 CreatorName Gondo, Yasuyuki
	NRID 1000050721396 1000050721396 researchmap permalink 7000009476 7000009476 ORCID 0000-0003-1272-7090 0000-0003-1272-7090 Scopus Author ID 7405310823 7405310823 CreatorName Yamamoto, Michio
	NRID 1000040114516 1000040114516 ORCID 0000-0002-0203-5587 0000-0002-0203-5587 researchmap permalink read0043227 read0043227 Scopus Author ID 55628569951 55628569951 CreatorName Suzuki, Takashi
	CreatorName Miura, Rina
	NRID 1000050335444 1000050335444 ORCID 0000-0002-2369-3095 0000-0002-2369-3095 CreatorName Sakurai, Takashi
	NRID 1000090507013 1000090507013 ORCID 0000-0002-2761-3240 0000-0002-2761-3240 CreatorName Takeda, Akinori
	NRID 1000080501212 1000080501212 ORCID 0000-0001-7810-8984 0000-0001-7810-8984 CreatorName Arahata, Yutaka
Description	Shinozaki M, Hishida H, Gondo Y, et al. Machine learning model for predicting the conversion to dementia using the Cube Copying Test. Journal of Alzheimer’s Disease. 2025;108(1_suppl):S141-S158. doi:10.1177/13872877251376939
Abstract	Background: Early detection of dementia requires highly accurate and efficient screening tests that minimize patient burden. Objective: To develop a machine learning model predicting dementia conversion within 3–5 years using Cube Copying Test (CCT) drawings at baseline. Methods: This retrospective study analyzed CCT drawing data from 767 patients at the Center for Comprehensive Care and Research on Memory Disorders (2011–2020). Of the 2303 patients who met the inclusion criteria, 534 were excluded due to mild cognitive impairment (MCI) persistence, pending diagnoses, or new neurovascular diseases, while 1002 were lost to follow-up. Eligibility criteria included a baseline Mini-Mental State Examination (MMSE) score ≥24, absence of dementia diagnosis or anti-dementia medication intake, and completion of a 3–5-year follow-up without meeting exclusion criteria. Results: Of 767 patients, 457 converted to dementia (318 with Alzheimer’s disease, 116 with dementia with Lewy bodies, and 23 with frontotemporal dementia) within 3–5 years, while 310 did not. The model achieved an area under the curve of 0.85 for predicting dementia conversion. Shapley Additive exPlanations analysis identified PatchCore-derived features as the strongest predictors, distinguishing drawing patterns of converters and non-converters. Conclusions: In patients who convert to Alzheimer’s disease, dementia with Lewy bodies, or frontotemporal dementia, the very early stages of constructional apraxia-like symptoms already exist at the preclinical stage or MCI stage. Applying deep learning-based anomaly-detection models can detect these early drawing distortions that differ from normal aging and contribute to improving the performance of dementia-conversion prediction.
Publisher	SAGE Publications
Source Title	Journal of Alzheimer’s Disease
Volume (Issue)	108(1_suppl.)
Page	141-158
Date of Issued	2025-09-22
Language	English
Handle URL	https://hdl.handle.net/11094/102844
PISSN	13872877
eISSN	18758908
Relation.isIdenticalTo	DOI (Publisher Version) https://doi.org/10.1177/13872877251376939
Access Rights	open access
CopyRight	This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
oaire:version	Version of Record
Category	学術雑誌論文 Journal Article

Resource Type	journal article
Local Type	Journal Article
DCMI Type	Text
DCTERMS.bibliographicCitation	Journal of Alzheimer’s Disease. 2025, 108(1_suppl.), p. 141-158
DC.title	Machine learning model for predicting the conversion to dementia using the Cube Copying Test
DC.creator	Shinozaki, Mio
	Hishida, Hiroyuki
	Gondo, Yasuyuki
	Yamamoto, Michio
	Suzuki, Takashi
	Miura, Rina
	Sakurai, Takashi
	Takeda, Akinori
	Arahata, Yutaka
DC.publisher	SAGE Publications
DC.language' scheme='DCTERMS.RFC1766	English
DC.type' scheme='DCTERMS.DCMIType	Text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2025-09-22
DC.identifier	https://doi.org/10.1177/13872877251376939
DC.description	Shinozaki M, Hishida H, Gondo Y, et al. Machine learning model for predicting the conversion to dementia using the Cube Copying Test. Journal of Alzheimer’s Disease. 2025;108(1_suppl):S141-S158. doi:10.1177/13872877251376939
DCTERMS.abstract	Background: Early detection of dementia requires highly accurate and efficient screening tests that minimize patient burden. Objective: To develop a machine learning model predicting dementia conversion within 3–5 years using Cube Copying Test (CCT) drawings at baseline. Methods: This retrospective study analyzed CCT drawing data from 767 patients at the Center for Comprehensive Care and Research on Memory Disorders (2011–2020). Of the 2303 patients who met the inclusion criteria, 534 were excluded due to mild cognitive impairment (MCI) persistence, pending diagnoses, or new neurovascular diseases, while 1002 were lost to follow-up. Eligibility criteria included a baseline Mini-Mental State Examination (MMSE) score ≥24, absence of dementia diagnosis or anti-dementia medication intake, and completion of a 3–5-year follow-up without meeting exclusion criteria. Results: Of 767 patients, 457 converted to dementia (318 with Alzheimer’s disease, 116 with dementia with Lewy bodies, and 23 with frontotemporal dementia) within 3–5 years, while 310 did not. The model achieved an area under the curve of 0.85 for predicting dementia conversion. Shapley Additive exPlanations analysis identified PatchCore-derived features as the strongest predictors, distinguishing drawing patterns of converters and non-converters. Conclusions: In patients who convert to Alzheimer’s disease, dementia with Lewy bodies, or frontotemporal dementia, the very early stages of constructional apraxia-like symptoms already exist at the preclinical stage or MCI stage. Applying deep learning-based anomaly-detection models can detect these early drawing distortions that differ from normal aging and contribute to improving the performance of dementia-conversion prediction.
DC.rights	This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
DC.format	102844
citation_title	Machine learning model for predicting the conversion to dementia using the Cube Copying Test
citation_author	Shinozaki, Mio
	Hishida, Hiroyuki
	Gondo, Yasuyuki
	Yamamoto, Michio
	Suzuki, Takashi
	Miura, Rina
	Sakurai, Takashi
	Takeda, Akinori
	Arahata, Yutaka
citation_publisher	SAGE Publications
citation_language	English
citation_date	2025-09-22
citation_journal_title	Journal of Alzheimer’s Disease
citation_volume	108
citation_issue	1_suppl.
citation_firstpage	141
citation_lastpage	158
citation_doi	https://doi.org/10.1177/13872877251376939
citation_public_url	https://hdl.handle.net/11094/102844

Machine learning model for predicting the conversion to dementia using the Cube Copying Test

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