A Fine Evaluation Method for Cube Copying Test for Early Detection of Alzheimer's Disease

2025-12-01

AI Summary

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.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Motion & Tracking

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Background: Impairment of visual spatial cognitive function is the most common early clinical manifestation of Alzheimer's Disease (AD). When the Montreal Cognitive Assessment (MoCA) uses the "0/1" binary method ("pass/fail") to evaluate the visual spatial cognitive ability represented by the Cube Copying Test(CCT), the elder with less formal education generally score 0 point, resulting in serious bias in the evaluation results. Therefore, this study proposes a fine evaluation method for CCT based on dynamic handwriting feature extraction of DH-SCSM-BLA. method : The Cogni-CareV3.0 software independently developed by our team was used to collect dynamic handwriting data of CCT. Then, the spatial and motion features of segmented dynamic handwriting were extracted, and feature matrix with unequal dimensions were normalized. Finally, a bidirectional long short-term memory network model combined with attention mechanism (BiLSTM-Attention) was adopted for classification. Result: The experimental results showed that: The proposed method has significant superiority compared to similar studies, with a classification accuracy of 86.69%. The distribution of cube drawing ability scores has significant regularity for three aspects such as MCI patients and healthy control group, age, and levels of education. It was also found that score for each cognitive task including cube drawing ability score is negatively correlated with age. Score for each cognitive task including cube drawing ability score, but positively correlated with levels of education significantly. Conclusion: This study provides a relatively objective and comprehensive evaluation method for early screening and personalized intervention of visual spatial cognitive impairment.

Problem

• Develops a fine evaluation method for Cube Copying Test to reduce bias in early Alzheimer's detection
• Extracts dynamic handwriting features to classify cognitive impairment more accurately than binary scoring
• Analyzes how cube drawing ability correlates with age, education, and cognitive status

Innovation

• Dynamic handwriting feature extraction for fine evaluation
• BiLSTM-Attention model for classification of cognitive impairment
• Normalization of unequal-dimension feature matrices