Evaluating deep learning techniques for identifying tongue features in subthreshold depression: a prospective observational study.

Objective: This study aims to evaluate the potential of using tongue image features as non-invasive biomarkers for diagnosing subthreshold depression and to assess the correlation between these features and acupuncture treatment outcomes using advanced deep learning models.

Methods: We employed five advanced deep learning models-DenseNet169, MobileNetV3Small, SEResNet101, SqueezeNet, and VGG19_bn-to analyze tongue image features in individuals with subthreshold depression. These models were assessed based on accuracy, precision, recall, and F1 score.