SynthesisBMC psychiatry2025
AI-based recognition of facial and micro-expressions for the diagnosis of mental and neurological disorders: a systematic review.
Synthesis in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- From fragmented neurotechnologies to closed-loop brain health systems: integrating artificial intelligence, digital health, digital twins, and advanced materials for brain disorders.Frontiers in bioengineering and biotechnology · 2026Article
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1 author.
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Abstract
introductionThe harnessing of advanced AI technologies allows us to analyze faces and minute details of facial expressions associated with neuro and psycho pathological conditions and create algorithms to automate diagnoses. The purpose of this review is to analyze what AI technologies have been developed and what is their use in the diagnosis of mental and neuro disorders through the automated recognition of facial expressions and minute details of their changes.
methodsA systematic search of the main scientific databases – PubMed, Scopus, Web of Science, ScienceDirect, IEEE Xplore and Google Scholar - was carried out on January 22, 2025. Only publications in English in the interval between 2021 and 2025 were included. The inclusion criteria were the use of AI methods on still or moving images of faces in the context of diagnosing mental and/or neuro disorders. The quality of the studies and the risk of bias were evaluated by appropriate appraisal tools developed for specific designs.
resultsIn this study, after reviewing 1710 initial articles, 36 relevant and eligible articles were selected for analysis. These articles mainly focused on diagnosing mental and neurological disorders such as autism, depression, and anxiety using artificial intelligence and facial feature analysis. The diversity of populations and sample sizes was considerable, and the input data included images, video, EEG, and fMRI. The reported diagnostic accuracy of AI models ranged from 80.5% to 99.9% (mean ≈ 93%), with F1-scores between 0.87 and 0.99 and AUC values mostly above 0.90. The most frequently used algorithms were convolutional neural networks (CNNs), transfer learning, and hybrid deep learning approaches.
conclusionThe results of the study indicate a significant growth in the use of artificial intelligence in the diagnosis of mental and neurological disorders, especially in autism, depression and anxiety. The use of facial and multimodal data combined with advanced algorithms has increased the accuracy of diagnosis.
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