The trial aimed to evaluate the accuracy of an artificial intelligence-based system for diagnosing depressive disorders. Specifically, it sought to determine whether the system's assessment validity is non-inferior to that of psychiatric specialists.
Subjects with anxiety disorders （1）
In accordance with ICD-11 for Depressive Disorders;
Between the ages of 18-60;
Ability to use computers or smartphone;
With severe psychiatric symptoms requiring hospitalization, or unable to complete the required assessment and treatment;
With a high risk of suicide or self-injury;
With severe physical diseases, central nervous system diseases, or substance abuse;
With intellectual, visual, or auditory impairments that affect their ability to interact with aided-diagnostic systems.
The study includes the subjects with depressive disorders and healthy control into the experiment. Depressive individuals for the study will be recruited from actual outpatient patients at the Shanghai Mental Health Center. The healthy control will be recruited through public advertisements, both online and offline. Psychiatrists will be recruited through public advertisements by the Shanghai Mental Health Center.
Case-control
Cross-sectional
Not listed
Diagnostic Accuracy (Sensitivity, Specificity, and Area Under the Curve) of the AI-Based Screening System for Depressive Disorders
In this study, the evaluation results of the subjects were taken as the gold standard based on the diagnostic conclusions of psychiatrists. The screening results of the aided-diagnostic system were compared with the diagnostic conclusions of the psychiatrists to verify the screening effectiveness of this system. Sensitivity : Calculated as \[True Positives / (True Positives + False Negatives)\], evaluating the AI system's ability to correctly identify patients with depression. Specificity : Calculated as \[True Negatives / (True Negatives + False Positives)\], evaluating the AI system's ability to correctly exclude non-depressed individuals.Area Under the Receiver Operating Characteristic Curve (AUC), which comprehensively evaluates the diagnostic discriminative power of the system by plotting sensitivity versus 1-specificity across different decision thresholds and calculating the AUC value .
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