Video analysis of hand gestures can help distinguish patients with carpal tunnel syndrome (CTS) in three sentences: Recording and analyzing grip and release gestures of 64 participants using machine learning algorithms allowed researchers to detect CTS with 90% sensitivity and 85% specificity, outperforming conventional screening methods. The analysis involved tracking 21 feature points on participants' hands during gestures to extract motion characteristics classified by support vector machines. With further evaluation, this technique may allow simple CTS screening using smartphones.