ReviewNpj mental health research2023
A systematic review on automated clinical depression diagnosis.
Review in Npj mental health research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial Intelligence in Diagnosing Depression Through Behavioural Cues: A Diagnostic Accuracy Systematic Review and Meta-Analysis.Journal of clinical nursing · 2026Pooled it
- Text-Based Depression Estimation Using Machine Learning With Standard Labels: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Diagnostic accuracy of traditional and deep learning methods for detecting depression based on speech features: a systematic review and meta-analysis.BMC psychiatry · 2025Pooled it
- Unraveling the associations between voice pitch and major depressive disorder: a multisite genetic study.Molecular psychiatry · 2025Pooled it
- Analysis of EEG microstate transition patterns and multi-instance learning-based recognition model for depression.Medical & biological engineering & computing · 2026Article
- Cross-linguistic benchmarking of NLP metrics for psychosis research.NPJ digital medicine · 2026Article
- A systematic review on mindfulness-based immersive interventions in depressive disorders.Npj mental health research · 2026Review
- Language-based detection of depression with machine learning: systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Deep neural network-based analysis of voice biomarkers for monitoring treatment response in adolescent major depressive disorder.Communications medicine · 2026Article
- Interpretable depression assessment using a large language model.PLOS digital health · 2026Article
- Multimodal behavioral phenotyping for depressive-spectrum classification and severity estimation using eye tracking, facial behavior, and transcript-derived language.Frontiers in psychiatry · 2026Article
- Exploring Acoustic Correlates of Depression and Preliminary Screening Models Using XGBoost and SHAP.Behavioral sciences (Basel, Switzerland) · 2025Article
- Bridging Gaps in Sundown Syndrome Research: a Scoping Review and Roadmap for Future Multimodal Approaches.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2025Article
- Language-Based Detection of Depression with Machine Learning: Systematic Review and Meta- Analysis.Research square · 2025Article
- Systematic Review and Meta-Analysis of Explainable Machine Learning Models for Clinical Depression Detection.Behavioral sciences (Basel, Switzerland) · 2025Review
- Using a fine-tuned large language model for symptom-based depression evaluation.NPJ digital medicine · 2025Article
- Acoustic signatures of depression elicited by emotion-based and theme-based speech tasks.BMJ mental health · 2025Article
- Integration of Multi-Modal Biosensing Approaches for Depression: Current Status, Challenges, and Future Perspectives.Sensors (Basel, Switzerland) · 2025Review
- Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study.JMIR formative research · 2025Article
- Acceptability, Usability, and Insights Into Cybersickness Levels of a Novel Virtual Reality Environment for the Evaluation of Depressive Symptoms: Exploratory Observational Study.JMIR formative research · 2025Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Assessing mental health disorders and determining treatment can be difficult for a number of reasons, including access to healthcare providers. Assessments and treatments may not be continuous and can be limited by the unpredictable nature of psychiatric symptoms. Machine-learning models using data collected in a clinical setting can improve diagnosis and treatment. Studies have used speech, text, and facial expression analysis to identify depression. Still, more research is needed to address challenges such as the need for multimodality machine-learning models for clinical use. We conducted a review of studies from the past decade that utilized speech, text, and facial expression analysis to detect depression, as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline. We provide information on the number of participants, techniques used to assess clinical outcomes, speech-eliciting tasks, machine-learning algorithms, metrics, and other important discoveries for each study. A total of 544 studies were examined, 264 of which satisfied the inclusion criteria. A database has been created containing the query results and a summary of how different features are used to detect depression. While machine learning shows its potential to enhance mental health disorder evaluations, some obstacles must be overcome, especially the requirement for more transparent machine-learning models for clinical purposes. Considering the variety of datasets, feature extraction techniques, and metrics used in this field, guidelines have been provided to collect data and train machine-learning models to guarantee reproducibility and generalizability across different contexts.
Identifiers
What OpenQuestion holds
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.