ArticleIEEE transactions on bio-medical engineering2021
Classifying Major Depressive Disorder and Response to Deep Brain Stimulation Over Time by Analyzing Facial Expressions.
Article in IEEE transactions on bio-medical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 3 syntheses or guidelines pooled it, 59 citations in OpenAlex.
- Deep learning in neurosurgery: a systematic literature review with a structured analysis of applications across subspecialties.Frontiers in neurology · 2025Pooled it
- Non-verbal behaviours as predictors for treatment response in patients with depression or schizophrenia: a systematic review.Frontiers in psychiatry · 2025Pooled it
- Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review.PloS one · 2022Pooled it
- Prefrontal-bed nucleus of the stria terminalis physiological and neuropsychological biomarkers predict therapeutic outcomes in depression.Nature communications · 2025Trial
- Identification of suicidal ideation in major depressive disorder: A machine learning approach with multimodal digital features.PLOS digital health · 2026Article
- Detecting cognitive impairment and psychological well-being among older adults.Machine learning. Health · 2026Article
- Facial Expressions as a Nexus for Health Assessment.Bioengineering (Basel, Switzerland) · 2026Review
- Explainability in AI-enabled medical neurotechnology: a scoping review.Journal of neuroengineering and rehabilitation · 2026Article
- Facial Expression Metrics as Digital Biomarkers of Neurologic Disease.Neurology open access · 2025Article
- Facial expression analysis and machine learning for objective assessment of alcohol craving in alcohol use disorder: a study protocol.Frontiers in psychiatry · 2025Article
- Mild cognitive impairment detection from facial video interviews by applying spatial-to-temporal attention module.Expert systems with applications · 2024Article
- Evaluating and mitigating unfairness in multimodal remote mental health assessments.PLOS digital health · 2024Article
- Deep brain stimulation for refractory major depressive disorder: a comprehensive review.Molecular psychiatry · 2024Review
- Multimodal Mental Health Digital Biomarker Analysis From Remote Interviews Using Facial, Vocal, Linguistic, and Cardiovascular Patterns.IEEE journal of biomedical and health informatics · 2024Article
- A Deep Learning Driven Simulation Analysis of the Emotional Profiles of Depression Based on Facial Expression Dynamics.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2024Article
- Digital Phenotyping for Differential Diagnosis of Major Depressive Episode: Narrative Review.JMIR mental health · 2023Review
- HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics.Sensors (Basel, Switzerland) · 2023Article
- Public mental health through social media in the post COVID-19 era.Frontiers in public health · 2023Article
- Multimodal Assessment of Schizophrenia and Depression Utilizing Video, Acoustic, Locomotor, Electroencephalographic, and Heart Rate Technology: Protocol for an Observational Study.JMIR research protocols · 2022Article
- Deep Brain Stimulation for Depression.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2022Review
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 1 country.
Funding
Abstract
objectiveMajor depressive disorder (MDD) is a common psychiatric disorder that leads to persistent changes in mood and interest among other signs and symptoms. We hypothesized that convolutional neural network (CNN) based automated facial expression recognition, pre-trained on an enormous auxiliary public dataset, could provide improve generalizable approach to MDD automatic assessment from videos, and classify remission or response to treatment.
methodsWe evaluated a novel deep neural network framework on 365 video interviews (88 hours) from a cohort of 12 depressed patients before and after deep brain stimulation (DBS) treatment. Seven basic emotions were extracted with a Regional CNN detector and an Imagenet pre-trained CNN, both of which were trained on large-scale public datasets (comprising over a million images). Facial action units were also extracted with the Openface toolbox. Statistics of the temporal evolution of these image features over each recording were extracted and used to classify MDD remission and response to DBS treatment.
resultsAn Area Under the Curve of 0.72 was achieved using leave-one-subject-out cross-validation for remission classification and 0.75 for response to treatment.
conclusionThis work demonstrates the potential for the classification of MDD remission and response to DBS treatment from passively acquired video captured during unstructured, unscripted psychiatric interviews. SIGNIFICANCE: This novel MDD evaluation could be used to augment current psychiatric evaluations and allow automatic, low-cost, frequent use when an expert isn't readily available or the patient is unwilling or unable to engage. Potentially, the framework may also be applied to other psychiatric disorders.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.