ai-assisted rapid warning for mental disorders based on high-resolution fundus imaging and high-speed eye-tracking

Conditions: Mental Disorder

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.

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.

Eligibility criteria:
inclusion criteria: * be aged 14 to 45-year-old * have had at least 6-years of primary education * be drug-naïve * be understanding the survey, be willing to enrol in the study and sign the informed consent * through the structured interview for prodromal syndromes/scale of prodromal symptoms (sips/sops), the participants should meet the criteria of prodromal syndrome. participants should fulfil at least one of the prodromal syndrome criteria: (1) brief intermittent psychotic syndrome, (2) attenuated positive symptom syndrome, or (3) genetic risk and deterioration syndrome * meets icd-11 diagnostic criteria for schizophrenia * meets icd-11 diagnostic criteria for mood disorders * meets icd-11 diagnostic criteria for anxiety disorders * meets icd-11 diagnostic criteria for neurocognitive disorders exclusion criteria: * acute or chronic renal failure; liver cirrhosis or active liver diseases * abnormal laboratory tests results judged by the researchers to be clinically significant and considered to affect the efficacy of the test drugs or the safety of the subjects * severe or unstable physical diseases, including: neurological disorders (delirium, dementia, stroke, epilepsy, migraine, etc.), congestive heart failure, angina pectoris, myocardial infarction, arrhythmia, hypertension (including untreated or uncontrolled hypertension), malignant tumours, immune compromise, and blood glucose above 12 mmol/l * alcohol abuse within 30 days, or alcohol or drug dependence within 6 months before the trial * pregnant or lactating women, or women in childbearing age who are positive in urine human chorionic gonadotropin test, or men and women who do not take effective contraceptive measures or plan for pregnancy within 3 months after the initiation of the trial * stroke within the last month * participating in any clinical trial within 30 days before the baseline * other situations judged by the investigators not to be suitable for the clinical trial

Keywords: eye movement