AI-Assisted Rapid Warning for Mental Disorders Based on High-Resolution Fundus Imaging and High-Speed Eye-Tracking

This project aims to collect eye-tracking trajectories and fundus imaging data from individuals seeking mental health services. By utilizing artificial intelligence, combining dynamic (eye-tracking) and static (fundus) data, and employing convolutional neural network analysis methods, the investigators will develop models for the classification and early warning of common mental disorders. These models will assist clinicians in making objective diagnoses of common mental disorders and in predicting the risk of adverse outcomes, thereby addressing the significant technical bottleneck of the current lack of objective diagnostic and warning instruments for mental disorders.