Evidence map›Paper›PMID 41904340›Full record

ArticleNPJ digital medicine2026

Deep learning characterizes depression and suicidal ideation in young adults from eye movements.

Kleanthis Avramidis, Woojae Jeong, Aditya Kommineni, Sudarsana R Kadiri, Marcus Ma, Colin McDaniel, Myzelle Hughes, Thomas McGee, Elsi Kaiser, Dani Byrd and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Kleanthis AvramidisThomas Lord Department of Computer Science, University of Southern California, Los Angeles, USA. avramidi@usc.edu.
Woojae JeongMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, USA.
Aditya KommineniMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, USA.
Sudarsana R KadiriMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, USA.
Marcus MaThomas Lord Department of Computer Science, University of Southern California, Los Angeles, USA.
Colin McDanielBrain and Creativity Institute, University of Southern California, Los Angeles, USA.
Myzelle HughesBrain and Creativity Institute, University of Southern California, Los Angeles, USA.
Thomas McGeeDepartment of Psychology, University of California, Los Angeles, USA.
Elsi KaiserDepartment of Linguistics, University of Southern California, Los Angeles, USA.
Dani ByrdDepartment of Linguistics, University of Southern California, Los Angeles, USA.
Assal HabibiBrain and Creativity Institute, University of Southern California, Los Angeles, USA.
B Rael CahnDepartment of Psychiatry and Behavioral Sciences, University of Southern California, Los Angeles, USA.
Idan A BlankDepartment of Psychology, University of California, Los Angeles, USA.
Kristina LermanThomas Lord Department of Computer Science, University of Southern California, Los Angeles, USA.
Takfarinas MedaniMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, USA.
Richard M LeahyMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, USA.
Shrikanth NarayananThomas Lord Department of Computer Science, University of Southern California, Los Angeles, USA.

Funding

Defense Advanced Research Projects Agency N660012324006
6 · The paper itself

Abstract

Objective biobehavioral markers for mental health conditions remain elusive, with diagnosis typically relying on self-reports and clinical interviews. We investigate eye tracking as a potential marker of attentional and mood biases associated with symptoms of depression and suicidal ideation from self-reported screening questionnaires. We analyze eye movements from 126 young adults during reading and responding to emotionally loaded sentences. A deep learning framework was designed to account for intra-trial and inter-trial variations in eye movements, achieving an AUC of 0.793 (95% CI: 0.766-0.819) for identifying depression/suicidality against healthy controls, and 0.826 (95% CI: 0.798-0.853) for suicidality specifically. The model also exhibited moderate accuracy in differentiating depressed from suicidal individuals (AUC: 0.609, 95% CI: 0.569-0.646). Discriminative patterns were more pronounced during response generation and for stimuli of negative sentiment. These findings suggest that eye tracking can provide objective markers of self-reported symptom severity by measuring the impact of emotional stimuli on oculomotor control.

Identifiers

PMID41904340
PMCPMC13195078

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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 Socratic graph.