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 Concept Videos

Depression: Overview01:18

Depression: Overview

208
Depression is a prevalent mental illness marked by persistent sadness and lack of interest in previously enjoyable activities. It can take several forms, including major depression, persistent depressive disorder, and bipolar I and II disorders. Symptoms range from emotional changes like chronic worry to physical changes like sleep disturbances and suicidal thoughts. From a neurobiological perspective, depression is believed to be triggered by abnormalities in the brain's prefrontal cortex,...
208
Depressive Disorders: MDD and Dysthymia01:27

Depressive Disorders: MDD and Dysthymia

37
Depressive disorders are a group of mental health conditions characterized by pervasive feelings of sadness, diminished pleasure in life, and a significant impact on daily functioning. These conditions are most prevalent in individuals during their 30s and affect women at twice the rate of men. Contrary to popular belief, younger individuals are generally more susceptible to these disorders than older adults. Two key types of depressive disorders include Major Depressive Disorder (MDD) and...
37

You might also read

Related Articles

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

Sort by
Same author

pH-programmed precipitation with post-upgrading for integrated recovery of Be, Al, and ammonium sulfate from Be-bearing smelting residue.

Journal of environmental management·2026
Same author

Site-specific N-glycosylation alterations in serum proteins across subtypes of adolescent depressive disorder.

BMC psychiatry·2026
Same author

Stepwise enhanced photoelectrochemical activity of BiOCl by Fe(III)-doping and aptamer immobilization for ultrasensitive detection of tetracycline.

Food chemistry·2026
Same author

Reverse-engineered ratiometric sensor assisted by dual redox-active DNA intercalators for ultrasensitive and reliable detection of mercury(II).

Analytica chimica acta·2026
Same author

Retraction notice to"Corrigendum to '25-hydroxycholesterol promotes RANKL-induced osteoclastogenesis through coordinating NFATc1 and Sp1 complex in the transcription of miR-139-5p'" [Biochem. Biophys. Res. Commun. 532 (2020) 166].

Biochemical and biophysical research communications·2026
Same author

Retraction notice to "25-hydroxycholesterol promotes RANKL-induced osteoclastogenesis through coordinating NFATc1 and Sp1 complex in the transcription of miR-139-5p" [Biochem. Biophys. Res. Commun. 485 (2017) 736-741].

Biochemical and biophysical research communications·2026

Related Experiment Video

Updated: Jul 6, 2026

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method
07:12

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method

Published on: August 2, 2021

DepITCM: an audio-visual method for detecting depression.

Lishan Zhang1,2, Zhenhua Liu3, Yumei Wan3

  • 1Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China.

Frontiers in Psychiatry
|February 7, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multimodal deep learning model for depression detection, enhancing accuracy by integrating audio-visual data and multi-task learning. The proposed method effectively captures temporal, channel, and spatial features for improved screening.

Keywords:
DepITCMdepression detectionfeature extractionmulti-task learningmultimodal

More Related Videos

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder
05:19

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

Published on: July 7, 2023

Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment
08:25

Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment

Published on: December 6, 2024

Related Experiment Videos

Last Updated: Jul 6, 2026

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method
07:12

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method

Published on: August 2, 2021

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder
05:19

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

Published on: July 7, 2023

Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment
08:25

Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment

Published on: December 6, 2024

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision
  • Audio Signal Processing

Background:

  • Depression is a widespread mental health issue requiring effective early detection methods.
  • Current deep learning models for depression detection using audio-video data face challenges in feature extraction and fusion.
  • Limited research has simultaneously addressed temporal, channel, and spatial information for depression detection.

Purpose of the Study:

  • To propose a novel multimodal deep learning model for enhanced depression detection.
  • To improve feature extraction by focusing on temporal, channel, and spatial dimensions.
  • To leverage multi-task learning to boost the accuracy of depression classification.

Main Methods:

  • Developed the DepITCM model, incorporating data preprocessing, an Inception-Temporal-Channel Principal Component Analysis (ITCM) Encoder, and a multi-task learning module.
  • Employed a staged feature extraction strategy within the ITCM Encoder, moving from global to local features.
  • Integrated a secondary regression task to complement the primary depression classification task.

Main Results:

  • Achieved an F1 score of 0.823 and accuracy of 0.823 on the AVEC2017 dataset for classification.
  • Attained an F1 score of 0.816 and accuracy of 0.810 on the AVEC2019 dataset for classification.
  • Demonstrated strong performance in the regression task with RMSE of 6.10 (AVEC2017) and 4.89 (AVEC2019), outperforming existing methods.

Conclusions:

  • The proposed DepITCM model effectively utilizes multimodal audio-video data for depression detection.
  • Multi-task learning significantly enhances the performance of depression detection models.
  • The model's ability to fuse temporal, channel, and spatial information provides a more comprehensive approach to depression screening.