A team from Shanghai Jiao Tong University and Kyoto University releases the first open Niacin Skin-Flushing Response dataset and applies an Efficient-Unet for precise area segmentation, then employs an SVM classifier to distinguish healthy controls from psychiatric patients based on normalized skin-flush metrics.

Key points

• Open NSR dataset: 600 photos from 120 subjects with binary masks and manual scores.
• Segmentation: Efficient-Unet achieved 91.31% Dice and 84.06% IoU without post-processing.
• Classification: SMOTE-balanced SVM with 5-fold CV reached 60–65% sensitivity and 75–88.3% specificity across psychiatric categories.

Why it matters: This device-independent AI approach offers a scalable, objective biomarker platform that could transform psychiatric diagnostics by reducing subjectivity and resource barriers.

A study published in BMC Psychiatry illustrates how machine learning models, particularly XGBoost, were applied to NHANES data to predict depressive symptoms in cognitively impaired older adults. Researchers identified key predictors such as general health and memory difficulties. This research offers a practical framework for early screening and intervention in geriatric mental health.