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.