Validation of an artificial intelligence-based drawing platform against the Clock Drawing Test for cognitive screening: a comparative study between Alzheimer's disease and healthy controls.

BACKGROUND: Traditional manual Clock Drawing Tests (CDTs) are widely used for cognitive screening but are constrained by human subjectivity and alphanumeric literacy or cultural barriers. OBJECTIVE: This study validated an open-source artificial intelligence (AI)-based drawing platform utilizing universal, pre-linguistic Lea symbols against manual CDT metrics for cognitive screening and differentiating Alzheimer's disease (AD) from healthy controls (HCs). METHODS: In this cross-sectional validation study, 111 participants aged ≥65 years, 55 clinically diagnosed AD patients and 56 HCs, were evaluated. Participants completed manual CDTs (6-point and 10-point scoring) and the AI-based drawing task on a tablet. Diagnostic accuracy was evaluated using receiver operating characteristic curve analysis, Cohen's kappa (κ), and multivariable logistic regression adjusted for age and gender. RESULTS: The AI model demonstrated clear diagnostic accuracy (area under the curve = 0.868), with overall accuracy comparable to the 10-point manual scale. An optimal diagnostic cut-off of <27.00 points, identified in this study, achieved 83.6% sensitivity and 75.0% specificity, demonstrating substantial agreement with clinical diagnosis (κ = 0.586, P < 0.001). Crucially, the AI-based drawing platform flagged 14 HCs (25.0%) as at-risk who were classified as intact by conventional manual CDT scales. Multivariable regression confirmed the AI score as a powerful independent predictor of AD status after adjusting for demographics (odds ratio: 0.940, 95% confidence interval: 0.904-0.979, P < 0.001). CONCLUSION: The AI-based drawing platform provides an objective, automated alternative to traditional CDTs. Its sensitivity to subtle visuoconstructional variations positions it as an effective, low-cost triage tool to optimize early neurological referrals in primary care.