NCT06511713

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

Artificial Intelligence-Assisted Rapid Warning for Mental Disorders Based on High-Resolution Fundus Images and High-Speed Eye-Tracking Trajectories

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 2,000 (estimated)
• Sponsor: Shanghai Mental Health Center · Academic / Other
• Sex: All
• Age: 15 Years – 45 Years
• Healthy volunteers: Not accepted

Summary

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.

Detailed description

The investigators have completed the construction of the eye-tracking diagnostic and warning system and have piloted the new system. The plan is to recruit 1,000 individuals at Clinical High Risk for Psychosis (CHR) for model validation of predictive outcomes, and 1,000 patients with common mental disorders for model validation of diagnostic classification. This cohort includes 300 patients with schizophrenia, 300 patients with affective disorders, 200 patients with anxiety disorders, and 200 patients with cognitive impairment in the elderly. The system will also directly connect with the investigators' previous research data collection system and be deployed in no fewer than one hospital's healthcare system. Additionally, variables that may affect the accuracy of results will be fine-tuned to ensure that the eye-tracking and fundus system more accurately reflects actual clinical conditions. The application of the system will revolve around a big data analysis platform and seamlessly integrate with the existing hospital information systems, designing real-time feedback report modules to assist clinicians in making objective diagnoses efficiently and effectively.

Conditions

• Mental Disorder

Interventions

Type	Name	Description
OTHER	routine clinical treatment	Participants will be informed that this is not a treatment study and it involves naturalistic follow-up without any extra intervention. They will otherwise follow the routine clinical treatment procedure.

Timeline

• Start date: 2024-07-20
• Primary completion: 2025-12-30
• Completion: 2025-12-30
• First posted: 2024-07-22
• Last updated: 2024-07-23

Locations

1 site across 1 country: China