使用立方体复制测试预测痴呆症转化的机器学习模型。

IF 3.1 3区 医学 Q2 NEUROSCIENCES
Mio Shinozaki, Hiroyuki Hishida, Yasuyuki Gondo, Michio Yamamoto, Takashi Suzuki, Rina Miura, Takashi Sakurai, Akinori Takeda, Yutaka Arahata
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引用次数: 0

摘要

背景:痴呆症的有效检测需要高度准确和有效的筛查试验,以尽量减少患者的负担。目的建立基于立方体复制测试(CCT)的机器学习模型,预测3-5年内痴呆的转化。方法:本回顾性研究分析了2011-2020年记忆障碍综合护理和研究中心767例患者的CCT图像数据。在符合纳入标准的2303例患者中,534例因持续轻度认知障碍(MCI)、待诊断或新发神经血管疾病而被排除,1002例因随访失败。入选标准包括基线迷你精神状态检查(MMSE)评分≥24分,无痴呆诊断或抗痴呆药物摄入,完成3-5年随访,不符合排除标准。结果767例患者中,457例在3-5年内转化为痴呆(318例为阿尔茨海默病,116例为路易体痴呆,23例为额颞叶痴呆),310例没有。该模型预测痴呆转换的曲线下面积为0.85。Shapley加性解释分析发现,patchcore衍生的特征是最强的预测因子,区分了转换者和非转换者的绘图模式。结论在转化为阿尔茨海默病、路易体痴呆或额颞叶痴呆的患者中,早在临床前阶段或MCI阶段就已经存在构象失用症样症状。应用基于深度学习的异常检测模型可以检测这些不同于正常老化的早期绘画扭曲,有助于提高痴呆转换预测的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
Journal of Alzheimer's Disease
Journal of Alzheimer's Disease 医学-神经科学
CiteScore
6.40
自引率
7.50%
发文量
1327
审稿时长
2 months
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.
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