AI-based Multi-center Research on Identification/Classification/Aided Diagnosis of Mood Disorder

Recognition/Classification/Auxiliary Diagnosis of Affective Disorder Based on AI:A Multi-center Study

At present, diagnosis and recognition of depression and bipolar disorder are mainly based on subjective evidence such as clinical interview and scale evaluation. The corresponding diagnosis basis has some shortcomings, such as poor diagnostic reliability and failure in early identification of bipolar disorder. Therefore, it is of great significance to explore objective diagnostic indicators to remedy the deficiencies.

Therefore，the investigators collect psychological and physiological information data of patients with bipolar disorder and depression.Then the investigators aim to construct and verify the multidimensional emotion recognition model to analyze the personality characteristics, negative emotions and cognitive reactions of different individuals, and form a systematic accurate recognition and evaluation tool.

Study Overview

• Status: Recruiting
• Conditions: Major Depression; Bipolar Disorder Depression

• Study Type: Observational
• Enrollment (Anticipated): 960

Contacts and Locations

• Study Contact: Name: Hu ShaoHua, M.D; Phone Number: +86 13357169115; Email: dorhushaohua@zju.edu.cn
• Study Contact Backup: Name: Lv HaiLong, M.D; Phone Number: +86 13858007467; Email: hailonglyu@zju.edu.cn

Study Locations

• China
  • Zhejiang
    • Hangzhou, Zhejiang, China, 310000
      • Recruiting
      • The First Affiliated Hospital of Zhejiang University
      • Contact: Hu ShaoHua; Phone Number: 13957162903; Email: dorhushaohua@zju.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 15 years to 55 years (Child, Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

The study will be conducted primarily at the First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China, starting from October 1, 2021, with an estimated total enrollment of 120 participants. Other participating centers include Peking University Sixth Hospital, At the Third Affiliated Hospital of Sun Yat-sen University, At the Second Affiliated Hospital of Kunming Medical University, At Wuhan People's Hospital, At Xiaoshan Hospital in Zhejiang Province, At Wenzhou seventh People's Hospital, At Ruian Fifth People's Hospital, respectively an evaluated 120 participants to be recruited.All research centers of the participants are from China. In screening interviews, the above criteria were carefully examined by two independent investigators.

Description

Inclusion Criteria:

1. Age 15-55, regardless of gender;
2. The brief International Neuropsychiatric Interview Chinese version (MINI) was used to meet the diagnostic criteria for DSM-IV-TR depressive disorder or bipolar disorder (type I);
3. Total score of Hamilton Depression Scale (HAMD-17) ≥17, and Young's Manic Scale (YMRS) ≤6;
4. Junior high school or above.

Exclusion Criteria:

1. The patient conforms to DSM-IV schizophrenia and related spectrum disorders.
2. The patient has a history of severe head trauma (loss of consciousness for more than 5 minutes), current or previous history of epilepsy, intracranial hypertension, or other serious neurological diseases;
3. Had a history of alcohol or psychoactive substance abuse/dependence in the 6 months prior to the test;
4. Those considered unsuitable for inclusion by the researcher.

Study Plan

How is the study designed?

Design Details

• Observational Models: Case-Control
• Time Perspectives: Cross-Sectional

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
facial action unit detection	The research recruits subjects to look at pictures and videos and then use cameras to record facial microexpressions. Finally, the research uses machine learning methods to analyze facial micro-expressions. Facial micro-expressions (MEs) are involuntary movements of the face that occur spontaneously when a person experiences an emotion but attempts to suppress or repress the facial expression, typically found in a high-stakes environment.	Baseline
event-related potentials	An electroencephalogram (EEG) is a test that measures electrical activity in the brain using small, metal discs (electrodes) attached to the scalp. Brain cells communicate via electrical impulses and are active all the time, even during asleep. This activity shows up as wavy lines on an EEG recording. The research recruits subjects to look at videos and pictures and use electroencephalography to record event-related potentials. Finally, we use time domain analysis and frequency analysis to get the results	Baseline
Galvanic skin response	The skin also has electrical activity, which is in constant, slight variation, and can be measured and charted. The skin's electrical conductivity fluctuates based on certain bodily conditions, and this fluctuation is called the galvanic skin response.We recruited subjects to watch videos and pictures and record galvanic skin response. Finally, we use time domain analysis and frequency analysis to get the results	Baseline

Collaborators and Investigators

• Sponsor: First Affiliated Hospital of Zhejiang University

Study record dates

Study Major Dates

• Study Start (Actual): December 1, 2022
• Primary Completion (Anticipated): December 1, 2024
• Study Completion (Anticipated): December 1, 2025

Study Registration Dates

• First Submitted: September 28, 2022
• First Submitted That Met QC Criteria: November 7, 2022
• First Posted (Actual): November 8, 2022

Study Record Updates

• Last Update Posted (Estimate): March 10, 2023
• Last Update Submitted That Met QC Criteria: March 9, 2023
• Last Verified: March 1, 2023

More Information

Terms related to this study

• Keywords: Electroencephalogram; Major Depression; Machine Learning; Bipolar Disorder Depression; Skin Potential; Micro-Facial Expression
• Additional Relevant MeSH Terms: Behavioral Symptoms; Mental Disorders; Pathologic Processes; Mood Disorders; Bipolar and Related Disorders; Depression; Depressive Disorder; Disease; Bipolar Disorder; Depressive Disorder, Major
• Other Study ID Numbers: IIT20210021B-R2

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No