Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 20, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Comparative Efficacy of MultiModal AI Methods in Screening for Major Depressive Disorder: Machine Learning Model

Donghao Chen1, Pengfei Wang2,3, Xiaolong Zhang2,3

  • 1School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.

JMIR Formative Research
|May 30, 2025
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Discovery of a potential novel pharmacogenomic biomarker on ANK3 gene for liafensine, a triple reuptake inhibitor for treatment-resistant depression.

Translational psychiatry·2026
Same author

Distinct Trajectories of Amygdala Connectivity Patterns Characterize Remission vs. Non-Remission in Patients With Major Depressive Disorder.

Depression and anxiety·2026
Same author

Associations between adverse childhood experiences and progression to incident psychiatric disorders in older adults: A 22-year cohort study.

Translational psychiatry·2026
Same author

Prefrontal Structural Asymmetry Mediates Body Mass Index and Treatment Response in Major Depressive Disorder.

Depression and anxiety·2026
Same author

Contribution of Longitudinal Mobile Health Measures in the Dynamic Track of Patients With Major Depressive Disorder: Multiple Centers, Prospective Cohort Study Using Functional Data Analysis and Machine Learning.

JMIR mHealth and uHealth·2026
Same author

Service Users' Views on Digital Remote Monitoring for Psychosis: Survey Study.

JMIR human factors·2026
Same journal

Effects of Virtual Reality on Postoperative Pain Management Following Minimally Invasive Gynecologic Surgery: Randomized Controlled Trial.

JMIR formative research·2026
Same journal

Prediction of Clinically Significant Depressive Symptoms at 2-Year Follow-Up in Older Adults: Machine Learning Study Using the English Longitudinal Study of Ageing.

JMIR formative research·2026
Same journal

Awareness, Educational Needs, and Curriculum Preferences Regarding AI and Medical Big Data Education Among Clinical Medicine Undergraduates: Cross-Sectional Survey Study.

JMIR formative research·2026
Same journal

Stakeholder Experiences With the Pneumococcal Conjugate Vaccine Chatbot as a Complementary Capacity-Building Tool for Frontline Health Workers in India: Qualitative Study.

JMIR formative research·2026
Same journal

Acceptability and Perceived Usefulness of a Digital Gambling Harm Minimisation Tool: A Cross-Sectional Study.

JMIR formative research·2026
Same journal

Knowledge Graphs Based on Meta-Analysis Papers Improve the Quality of Case Formulation: Mixed Methods Design.

JMIR formative research·2026
See all related articles
This summary is machine-generated.

Artificial intelligence (AI) analysis of audiovisual signals shows promise for major depressive disorder (MDD) screening. The question and answering (Q&A) paradigm demonstrated higher efficacy than mental imagery description (MID) for objective MD screening.

Area of Science:

  • Psychiatry
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Major depressive disorder (MDD) screening traditionally relies on subjective self-rated scales and clinical interviews.
  • Artificial intelligence (AI) offers potential for objective psychiatric assessment using audiovisual signals.

Purpose of the Study:

  • To evaluate the efficacy of different AI-driven paradigms for analyzing audiovisual signals in MDD screening.
  • To compare the performance of conventional scale (CS), question and answering (Q&A), mental imagery description (MID), and video watching (VW) paradigms.

Main Methods:

  • Recruited 89 participants (41 with MDD, 48 asymptomatic).
  • Developed AI models analyzing facial movement, acoustic, and text features from videos.
  • Utilized ablation experiments and 5-fold cross-validation with two AI methods.
Keywords:
MDDartificial intelligencecomputational psychiatryfacial action unitmajor depressive disordermultimodal analysismultiparadigm analysis

Related Experiment Videos

Last Updated: Jun 20, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Main Results:

  • The Q&A paradigm showed higher sensitivity (79.06%) than MID (P=.03) in video clip analysis.
  • Combining Q&A and MID improved individual-level accuracy (80.00%) compared to MID alone (P=.01).
  • AI models achieved over 76.25% binary accuracy for video predictions and 74.12% for individual predictions.

Conclusions:

  • The Q&A paradigm is more effective than MID for MDD screening, individually and combined.
  • AI analysis of audiovisual signals across multiple paradigms shows potential as an effective tool for objective MDD screening.