A Fine Evaluation Method for Cube Copying Test for Early Detection of Alzheimer's Disease
The Cube Copying Test (CCT) in the Montreal Cognitive Assessment (MoCA) employs a binary pass/fail scoring scheme, leading to floor effects—particularly among low-education older adults—and introducing significant bias in visuospatial cognitive assessment. Method: We propose a fine-grained CCT scoring framework based on dynamic handwriting features: trajectories are captured via Cogni-CareV3.0 during cube copying; spatiotemporal motor and geometric spatial features are extracted; an unequal-dimension feature normalization strategy is designed; and a BiLSTM-Attention fusion model is developed for early mild cognitive impairment (MCI) detection. Contribution/Results: Our approach overcomes the limitations of binary evaluation by establishing an age-negative- and education-positive-correlated continuous scoring scale. It achieves 86.69% classification accuracy—substantially outperforming prior methods—and uncovers systematic distribution patterns of cube-drawing ability in MCI identification, thereby enhancing screening objectivity and enabling personalized intervention.