Automatic Depression Assessment using Machine Learning: A Comprehensive Survey

Hatice Gunes; Jiaee Cheong; Michel Valstar; Rui Gao; Shiqing Tang; Siyang Song; Yupeng Huo

REVIEW 2 major objections 5 minor 300 references

Automatic Depression Assessment using Machine Learning: A Comprehensive Survey

T0 review · 2 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper claims to be the first comprehensive survey of machine-learning-based automatic depression assessment, spanning EEG and MRI as well as facial, body, audio, and language behaviours.

desk verdict A broad, useful ADA survey whose main overclaim—no existing interpretability work—contradicts its own citations, and whose search protocol needs one more pass before 'comprehensive' sticks. read the letter →

arxiv 2506.18915 v2 pith:6FH4M7ZV submitted 2025-06-09 q-bio.NC cs.AIcs.LG

Siyang Song , Yupeng Huo , Shiqing Tang , Jiaee Cheong , Rui Gao , Michel Valstar , Hatice Gunes This is my paper

classification q-bio.NCcs.AIcs.LG

keywords automaticdepressionassessmentmachinelearningdeepmultimodalbehaviouranalysisEEGandMRIbrainactivityfacialbodyspeechlanguagecuesADAdatasetscompetitions

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims to be the first survey to treat automatic depression assessment (ADA) as one field spanning all depression-relevant behaviour modalities: internal brain activity from EEG and MRI, plus external facial, body, audio, and language cues. The authors reviewed 1679 candidate papers and included 183 methods published between 2012 and 2024, organising them by modality, feature type, and predictor, and linking each technical family to the psychological and physiological evidence for why that behaviour carries depression information. If the coverage is right, the survey gives researchers a single map of what has been tried, where the gaps are, and what is actually holding the field back—not model creativity, but small datasets with self-reported labels and unstandardised evaluation.

What carries the argument

The organising device is a modality-by-modality taxonomy (EEG, MRI, facial/head, body, audio, language, and multimodal), each reviewed with its typical feature extractors and predictors. The survey is carried by a PRISMA-style systematic search across seven digital libraries that yielded 1,679 papers and 183 included studies, plus an evidence review (Sec. 2.2) that connects each modality to concrete depression biomarkers such as EEG alpha asymmetry, reduced positive facial expressions, monotonous speech, and increased first-person pronouns. This evidence base is what justifies treating the surveyed models as capturing real depression signals rather than dataset artefacts.

What would settle it

A reader could re-run the stated search across the same seven digital libraries (IEEE Explore, ACM Digital Library, Elsevier, Springer, ACL Anthology, IET, MIT Press) with the stated inclusion window of 2012-2024 and eligibility criteria, and check whether the 183 included papers match the yield; any significant set of qualifying peer-reviewed ADA papers that are missing would falsify the comprehensiveness claim.

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Extended reading notes

Core claim

The central claim is that machine-learning-based depression assessment has become a genuinely multi-modal field, and that this is the first review to capture that breadth. The survey asserts that depression is expressed both internally, in brain structure and electrical activity, and externally, in the face, body, voice, and word choice, and that each modality has produced distinct technical pipelines—from hand-crafted features with classical regressors to CNNs, transformers, and graph networks. It further claims that the shared bottleneck across all modalities is data quality: public datasets are small, mostly under 200 participants, and depression labels come largely from self-report questionnaires rather than standardised clinical assessment, which limits both model performance and evaluation objectivity.

Load-bearing premise

The survey's claim to be comprehensive rests on the assumption that its literature search was exhaustive and that the 183 included papers fairly represent the 2012-2024 ADA literature; the full search queries and exclusion counts are only in the Supplementary Material, so a reader cannot independently reproduce the search.

Editorial extensions

If this is right

The modality taxonomy and the 183-method table give researchers a direct way to spot underexplored areas, such as body-based ADA and privacy-preserving de-identified inputs.
If the data bottleneck is as central as the survey argues, then better datasets—larger, clinically labelled, demographically diverse—matter more for ADA progress than new network architectures.
The survey's review of competitions shows that de-identified facial primitives and audio features have been workable benchmarks since AVEC 2016, supporting privacy-preserving ADA as a practical default.
The documented lack of interpretability and reproducibility in deep ADA models indicates that future evaluation should routinely report which behaviour cues drive predictions.
The observation that most audio and language methods are fused with visual cues suggests that future ADA systems will be inherently multimodal, making fusion strategy and fairness across demographic groups core design issues.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A testable extension of the survey's evidence review is whether behaviour-grounded hand-crafted features (e.g., spectral EEG features, AU 12/14 patterns, pause and pitch statistics, first-person pronouns) close the accuracy gap with end-to-end deep models on the same datasets.
If label subjectivity is the key data problem, then a shared collection protocol using clinician-administered instruments rather than self-report scales would be a concrete improvement; models trained on such data could be tested for cross-site and cross-cultural generalisation.
The paper's call for a common repository could be operationalised as a public leaderboard with fixed evaluation splits and fairness metrics, which would test whether reported accuracy differences between methods are stable or dataset-driven.
The geographic concentration of publications suggests that the field's comparative results may be fragile; an independent multi-site re-evaluation of top-performing methods would settle whether current rankings reflect generalisable skill or dataset bias.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a genuinely useful survey, probably the broadest modality coverage in the ADA survey genre. The EEG/MRI inclusion, plus the summary of depression-linked behavioural evidence in Section 2.2, is the new part, and it is done carefully. I would trust it as an entry point for a new researcher, and I would cite it.

The paper is not methodologically groundbreaking—it is a synthesis—but the synthesis is the contribution, and the modality breadth is real. The modality-by-modality organization, the summary table, and the dataset/competition review are all serviceable. The authors also make a reasonable case for why prior surveys were narrower.

Soft spots, in order of importance. First, Section 6.1 says 'no existing work has investigated the interpretability, explainability, nor reproducibility of such models,' but the survey itself cites explainable and interpretable ADA papers (for example, references [33] and [210], among others). That is an internal overstatement and should be fixed; it undercuts an otherwise sensible discussion. Second, the comprehensiveness claim in the abstract and conclusion rests on the Section 3 search, and the main text reports only that 1679 papers were retrieved and 183 included. The search strings, exclusion counts, and full PRISMA flow are deferred to the supplementary material, and the database list omits PubMed/Scopus/Web of Science. For EEG/MRI depression work, much of the literature is in clinical venues indexed by PubMed, so I share the stress-test concern that the corpus may be systematically skewed. This is not fatal—the included papers look representative enough for a survey of this scope—but it is a load-bearing assumption for the word 'comprehensive,' and the authors should either add the missing databases or soften the claim. Minor: Fig. 3 is labeled PRIMA, and there are small typos like 'DAIZ-WOZ,' but those are trivial.

The citation pattern is fine. Some self-citations appear, but the cited works are peer-reviewed and sit alongside many third-party papers; no circularity concern.

Who is it for: graduate students and researchers entering ADA, and anyone needing a modality map. It will not settle any scientific question, but it is a solid reference. I would send it to review, mainly because a corrected version is likely to be a heavily used survey.

Referee Report

2 major / 5 minor

Summary. The paper presents a survey of machine learning-based automatic depression assessment (ADA) methods, covering brain modalities (EEG, MRI), expressive modalities (face, body, audio, language), and multimodal combinations. It synthesizes 183 papers, reviews public datasets and AVEC challenge series, and discusses challenges and opportunities. The central claim is that this is the first comprehensive survey of ADA across both internal brain activities and external expressive behaviours.

Significance. If the survey is indeed comprehensive and accurate, it would be a valuable reference for a rapidly growing field: it aggregates 183 methods, organizes them by modality and ML approach, links depression biomarkers to computational features, and provides a central web repository. The paper also gives due attention to privacy, label quality, and fairness. However, its significance is conditional on the reproducibility of the search protocol and on the accuracy of its challenge statements, since the comprehensiveness claim is load-bearing.

major comments (2)

[Section 6.1] The passage under 'Lack of interpretability and reproducibility' states that 'no existing work has investigated the interpretability, explainability, nor reproducibility of such models.' This is directly contradicted by several papers reviewed in this survey, including Ref. [33] (Gahalawat et al., 'Explainable depression detection via head motion patterns'), Ref. [201] (Zhou et al., 'Visually interpretable representation learning'), Ref. [282] (Kacem et al., 'interpretable representations of motion dynamics'), and Ref. [210] (Moreno et al., 'Expresso-ai: An explainable video-based deep learning model'). The overstatement weakens the credibility of the challenges discussion and should be corrected to a more precise formulation, e.g., that few existing works systematically evaluate interpretability and reproducibility.
[Section 3] The central claim of being a 'comprehensive survey' rests on the exhaustiveness and representativeness of the literature search. However, the search databases listed (IEEE Xplore, ACM Digital Library, Elsevier, Springer, ACL Anthology, IET, MIT Press) omit PubMed/MEDLINE, Scopus, and Web of Science, which are the primary indexes for clinical EEG and MRI depression-classification studies. Since the exact search strings, PRISMA flow numbers, and exclusion counts are deferred to the Supplementary Material, and since Fig. 3 shows a flow diagram without stage-wise numbers in the main text, a reader cannot currently reproduce or audit the search. This leaves the 183-paper set potentially systematically skewed away from the very brain-imaging modalities the survey claims to cover. The authors should either justify the database choice, add the missing databases, or report the full search protocol and PRISMA counts in the main text or a clearly reproducible appendix.

minor comments (5)

[Fig. 3] The figure caption says 'PRIMA Schema' but should read 'PRISMA Schema.'
[Section 4.5.2] The phrase 'revealing subtle changes in energy overtime' should be 'revealing subtle changes in energy over time.'
[Section 4.3.1] The word 'behavuours' in 'these behavuours are more prone to data privacy' is a typo and should be 'behaviours.'
[Section 5.2] The text mentions 'DAIZ-WOZ depression dataset' twice; the correct acronym for the referenced dataset is DAIC-WOZ (Ref. [64]).
[Section 6.1] In the same paragraph on reproducibility, the paper lists several open-source implementations ([20], [28], [42], [69], [203], [215]), which is useful; consider making this a separate concrete point in the future-directions section rather than embedding it in the challenge statement.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey synthesizes an external literature and makes no derivation whose conclusion is equivalent to its inputs.

full rationale

This paper is a literature survey with no fitted parameters, no prediction targets, and no derived equations, so the characteristic circularity patterns (self-definition, fitted input renamed as prediction, ansatz smuggled via citation, uniqueness imported from authors) do not apply. Its central claim is that it provides a comprehensive, multi-modal review of automatic depression assessment (ADA) work from 2012–2024, supported by a PRISMA-style search protocol in Section 3. The paper does cite prior work by its own authors (e.g., Song et al. [42], [214], Cheong et al. [244], [300], [302]), but these citations are used as ordinary entries in the surveyed literature, alongside numerous third-party papers, and are not invoked as the logical basis for any conclusion. The claim of comprehensiveness rests on the search procedure (databases queried, eligibility criteria, selection process), not on any self-referential argument; the absence of PubMed/Scopus or of full stage-wise PRISMA counts in the main text is a completeness and reproducibility concern, not a circularity one. Since the survey makes no derivation whose conclusion reduces to its inputs, the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The survey's claims rest on the validity of behavioural depression biomarkers, on the representativeness of its literature sample, and on the acceptability of self-report depression labels used in the surveyed datasets. These are domain assumptions rather than mathematical axioms; no free parameters or invented entities are introduced.

assumptions (3)

domain assumption Depression is reflected in human brain and expressive behaviours (Section 2.2).
The survey's selection and organization of ADA approaches presupposes that these behavioural cues are valid depression indicators; weak or contradictory evidence for some cues (e.g., negative facial expressions) may undermine the surveyed approaches' rationale.
domain assumption The 183 included papers are a representative sample of ADA research.
Search queries and exclusion criteria are only partially specified in the main text, so the completeness claim cannot be fully audited.
domain assumption Self-reported depression severity scores (e.g., PHQ-8, BDI-II) are acceptable ground truth for evaluating ADA systems.
The survey reviews approaches that use these labels and notes their limitations in Section 5.

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Cite this review

Pith. "Pith review of Automatic Depression Assessment using Machine Learning: A Comprehensive Survey." pith.science (2026). https://pith.science/paper/6FH4M7ZV

@misc{pith2026250618915,
  author       = {Pith},
  title        = {Pith review of: Automatic Depression Assessment using Machine Learning: A Comprehensive Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6FH4M7ZV}},
  note         = {Machine review of arXiv:2506.18915}
}

read the original abstract

Depression is a common mental illness across current human society. Traditional depression assessment relying on inventories and interviews with psychologists frequently suffer from subjective diagnosis results, slow and expensive diagnosis process as well as lack of human resources. Since there is a solid evidence that depression is reflected by various human internal brain activities and external expressive behaviours, early traditional machine learning (ML) and advanced deep learning (DL) models have been widely explored for human behaviour-based automatic depression assessment (ADA) since 2012. However, recent ADA surveys typically only focus on a limited number of human behaviour modalities. Despite being used as a theoretical basis for developing ADA approaches, existing ADA surveys lack a comprehensive review and summary of multi-modal depression-related human behaviours. To bridge this gap, this paper specifically summarises depression-related human behaviours across a range of modalities (e.g. the human brain, verbal language and non-verbal audio/facial/body behaviours). We focus on conducting an up-to-date and comprehensive survey of ML-based ADA approaches for learning depression cues from these behaviours as well as discussing and comparing their distinctive features and limitations. In addition, we also review existing ADA competitions and datasets, identify and discuss the main challenges and opportunities to provide further research directions for future ADA researchers.

Figures

Figures reproduced from arXiv: 2506.18915 by the authors.

**Figure 1.** The negative impacts of depression. (2) Societal impacts: Depression also leads to serious social problems. More than half of the suicide cases in Europe are linked to depression-related mental disorders [88], with another study [89] revealed similar findings, i.e., the combined lifetime prevalence of depression and suicide attempts is 31%. Li et al. [90] surveyed 24 studies including 954,882 patients with depressio… view at source ↗

**Figure 2.** Illustration of common human behavioural biomarkers associated with depression. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** PRIMA Schema for our systematic review. the sentences expressed by depressed patients are usually fragmented or shortened, reflecting their reduced coherence and cognitive difficulty [137]. 2.2.6 Other depression-related human indicators In addition to the behaviours discussed above, depressed patients also show various cognitive deficits compared to non-depressed ones, including varied exploratory behaviours [138]… view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Statistics and trend of ADA approaches (2012-2024) surveyed in this paper. [PITH_FULL_IMAGE:figures/full_fig_p016_4.png]

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

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