{"title":"使用立方体复制测试预测痴呆症转化的机器学习模型。","authors":"Mio Shinozaki, Hiroyuki Hishida, Yasuyuki Gondo, Michio Yamamoto, Takashi Suzuki, Rina Miura, Takashi Sakurai, Akinori Takeda, Yutaka Arahata","doi":"10.1177/13872877251376939","DOIUrl":null,"url":null,"abstract":"<p><p>BackgroundEarly detection of dementia requires highly accurate and efficient screening tests that minimize patient burden.ObjectiveTo develop a machine learning model predicting dementia conversion within 3-5 years using Cube Copying Test (CCT) drawings at baseline.MethodsThis 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.ResultsOf 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.ConclusionsIn 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.</p>","PeriodicalId":14929,"journal":{"name":"Journal of Alzheimer's Disease","volume":" ","pages":"13872877251376939"},"PeriodicalIF":3.1000,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning model for predicting the conversion to dementia using the Cube Copying Test.\",\"authors\":\"Mio Shinozaki, Hiroyuki Hishida, Yasuyuki Gondo, Michio Yamamoto, Takashi Suzuki, Rina Miura, Takashi Sakurai, Akinori Takeda, Yutaka Arahata\",\"doi\":\"10.1177/13872877251376939\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>BackgroundEarly detection of dementia requires highly accurate and efficient screening tests that minimize patient burden.ObjectiveTo develop a machine learning model predicting dementia conversion within 3-5 years using Cube Copying Test (CCT) drawings at baseline.MethodsThis 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.ResultsOf 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.ConclusionsIn 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.</p>\",\"PeriodicalId\":14929,\"journal\":{\"name\":\"Journal of Alzheimer's Disease\",\"volume\":\" \",\"pages\":\"13872877251376939\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-09-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Alzheimer's Disease\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/13872877251376939\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Alzheimer's Disease","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/13872877251376939","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
Machine learning model for predicting the conversion to dementia using the Cube Copying Test.
BackgroundEarly detection of dementia requires highly accurate and efficient screening tests that minimize patient burden.ObjectiveTo develop a machine learning model predicting dementia conversion within 3-5 years using Cube Copying Test (CCT) drawings at baseline.MethodsThis 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.ResultsOf 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.ConclusionsIn 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.
期刊介绍:
The Journal of Alzheimer''s Disease (JAD) is an international multidisciplinary journal to facilitate progress in understanding the etiology, pathogenesis, epidemiology, genetics, behavior, treatment and psychology of Alzheimer''s disease. The journal publishes research reports, reviews, short communications, hypotheses, ethics reviews, book reviews, and letters-to-the-editor. The journal is dedicated to providing an open forum for original research that will expedite our fundamental understanding of Alzheimer''s disease.