ArticleSchizophrenia research2023
Detecting at-risk mental states for psychosis (ARMS) using machine learning ensembles and facial features.
Article in Schizophrenia research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The trial behind it
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Who cites it
4 citing papers in PubMed.
- Subtle impairments of facial emotion expressions in individuals at ultra-high risk for psychosis.Frontiers in psychiatry · 2026Article
- Collecting language, speech acoustics, and facial expression to predict psychosis and other clinical outcomes: strategies from the AMP® SCZ initiative.Schizophrenia (Heidelberg, Germany) · 2025Article
- MentalAId: an improved DenseNet model to assist scalable psychosis assessment.BMC psychiatry · 2025Article
- Natural language processing in at-risk mental states: enhancing the assessment of thought disorders and psychotic traits with semantic dynamics and graph theory.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
aimsOur study aimed to develop a machine learning ensemble to distinguish "at-risk mental states for psychosis" (ARMS) subjects from control individuals from the general population based on facial data extracted from video-recordings.
methods58 non-help-seeking medication-naïve ARMS and 70 healthy subjects were screened from a general population sample. At-risk status was assessed with the Structured Interview for Prodromal Syndromes (SIPS), and "Subject's Overview" section was filmed (5-10 min). Several features were extracted, e.g., eye and mouth aspect ratio, Euler angles, coordinates from 51 facial landmarks. This elicited 649 facial features, which were further selected using Gradient Boosting Machines (AdaBoost combined with Random Forests). Data was split in 70/30 for training, and Monte Carlo cross validation was used.
resultsFinal model reached 83 % of mean F1-score, and balanced accuracy of 85 %. Mean area under the curve for the receiver operator curve classifier was 93 %. Convergent validity testing showed that two features included in the model were significantly correlated with Avolition (SIPS N2 item) and expression of emotion (SIPS N3 item).
conclusionOur model capitalized on short video-recordings from individuals recruited from the general population, effectively distinguishing between ARMS and controls. Results are encouraging for large-screening purposes in low-resource settings.
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