大型视觉-语言模型在儿科运动抽动检测中的应用：病例对照诊断研究

BACKGROUND Clinical diagnosis of tic disorders (TDs) is often delayed by fluctuating symptoms and the subjective nature of assessments. While automated methods have been proposed to bridge this gap, traditional machine learning models frequently lack robustness in unconstrained environments and fail to capture the complex, multisite tics characteristic of severe TDs. OBJECTIVE This study aimed to develop and evaluate a fine-tuned large vision-language model framework for the automated, interpretable detection of pediatric motor tics, addressing the clinical need for objective and efficient sym