The goal of this observational study is to validate an AI algorithm's capability to differentiate the population with suicidal ideation from a control population using various multimodal variables, including voice analysis, facial emotions, natural language, and proteomics data. The primary research question it aims to answer is: Is it possible to identify suicidal ideation and suicide risk in adolescents early and non-intrusively using multimodal data analysis through digital instruments equipped with artificial intelligence? Participants in this study will be asked to: Complete psychometric instruments to establish a gold standard for detecting suicide risk and suicidal ideation. Provide voice recordings, facial emotion data, and linguistic content in natural and specific contexts. Participate in salivary proteomics data collection. This study compares three distinct groups: Ideation: Adolescent patients with current suicidal ideation. Clinical Population: Psychological or psychiatric patients of the same age and gender without suicidal ideation. General Population: Adolescents without known psychological or psychiatric pathology of the same age and gender, without suicidal ideation. Researchers will compare these groups to determine if the AI algorithm is effective in differentiating individuals with suicidal ideation (Group 1) from both a clinical control group (Group 2) and a general population control group (Group 3) using the collected multimodal data. The study aims to assess the algorithm's ability to identify early signs of suicide risk in these distinct participant populations.
Introduction: The Surveillance of Suicide Ideation in Adolescents (VISIA) project is a collaborative effort involving a diverse research team encompassing experts in mental disorders and seasoned researchers in data science, specializing in intelligent services and applications across various domains. The project aims to address the shortcomings identified in existing research. The initial phase of this endeavor seeks to validate an artificial intelligence (AI) algorithm capable of distinguishing individuals with suicidal ideation from a control group by analyzing multimodal variables. These variables include voice analysis, facial emotions, natural language, and proteomics. The cases will be labeled following established protocols. Hypothesis: The primary research question driving this project is: Can early and non-intrusive identification of suicidal ideation and suicide risk in young adults be achieved using multimodal data analysis through digital instruments equipped with artificial intelligence? Objectives: The overarching objective of this proposal is to design and implement an automated system for detecting emotional distress and suicide risk in adolescents aged between 11 and 16 using AI. This detection relies on molecular profiles derived from salivary proteomics, video and audio recordings in diverse contexts, and the linguistic content generated by the study participants. The integration of molecular profiles with data from video and audio recordings promises enhanced detection capabilities, providing a competitive edge in the development of future diagnostic tools. Specific Objectives: To realize the goals outlined above and tackle the scientific and technological challenges, the following specific objectives are established: Development of a gold standard for detecting suicide risk and suicidal ideation in adolescents. Identification of biomarkers (e.g., voice, facial expressions, salivary molecular fingerprint) relevant to characterizing adolescents with regard to suicide risk. Identification of pertinent multimodal features for characterizing adolescents in relation to suicide risk. Labeling of samples and AI model training. Methodology: Design and Study Subjects: The study adopts a non-interventionist, analytical, observational, and prospective approach. It encompasses three distinct groups for comparison: Ideation: Comprising individuals with current suicidal ideation. Clinical Population: Consisting of psychological or psychiatric patients of the same age and gender, devoid of suicidal ideation. General Population: Encompassing adolescents with no known psychological or psychiatric issues, matched in age and gender, and free of suicidal ideation. All three groups will undergo assessments using psychometric instruments, serving as a gold standard for AI system training and validation. In summary, the VISIA project endeavors to leverage artificial intelligence and multimodal data analysis to detect suicidal ideation and suicide risk in adolescents, offering the potential for early and non-intrusive identification. The study's comprehensive methodology, involving diverse subject groups and rigorous data collection, is poised to contribute significantly to the field of mental health research and diagnostic tool development.
Study Type
OBSERVATIONAL
Enrollment
339
Servizo Galego de Saúde
Santiago de Compostela, A Coruña, Spain
Hospital Álvaro Cunqueiro
Vigo, Pontevedra, Spain
University of Vigo
Vigo, Pontevedra, Spain
University Hospital Complex of Ourense
Ourense, Spain
Strengths and Difficulties Questionnaire (SDQ)
This questionnaire detects probable cases of mental and behavioral disorders in children and adolescents aged 4 to 17. A score \>19 points is considered positive in screening.
Time frame: through study completion, an average of 1 year
Mood and Feelings Questionnaire (MFQ)
This questionnaire assesses symptoms of depression/suicide risk. Scores above 27 points are considered positive for screening. Higher values are indicative of worse outcomes.
Time frame: through study completion, an average of 1 year
Paykel Suicide Scale (PSS)
This questionnaire asks about suicidal ideation over the past year. Scores equal to or greater than 4 are suggestive of suicidal ideation. Higher values are indicative of worse outcomes.
Time frame: through study completion, an average of 1 year
Facial microexpressions, voice and linguistic content analysis
Participants will be asked to speak in front of a camera about their emotional state over the past week. The recording will have a minimum duration of 2 minutes and will be used for voice analysis, facial microexpressions, and natural language processing (NLP) for linguistic content analysis. Participants will also be asked to write a brief text on the computer in response to questions about how they perceive themselves and how they think others perceive them. The maximum duration for this exercise is 10 minutes.
Time frame: through study completion, an average of 1 year
Proteomic and stress level analysis
The collection of saliva is performed in a simple, non-invasive, and painless manner, allowing for the subsequent retrieval of high-quality proteins for further analysis and studies.
Time frame: through study completion, an average of 1 year
Millon Adolescent Clinical Inventory - II (MACI-II)
A self-report questionnaire developed to evaluate the psychological functioning of adolescents. It assesses three domains in adolescent development. The average completion time is 30 minutes and can be conducted in groups.
Time frame: through study completion, an average of 1 year
Problematic Internet Use Scale in Adolescents (EUPI-A)
This 11-item questionnaire screens for problematic use of Internet among teenagers, ranginf fromm 0 to 44, with a cut-off for problematic use of 16 points. Higher values are indicative of worse outcomes.
Time frame: through study completion, an average of 1 year
European Bullying Intervention Project Questionnaire (EBIP-Q) and European Ciberbullying Intervention Project Questionnaire (ECIP-Q)
This tool for jointly assessing bullying and cyberbullying cointains The European Bullying Intervention Project Questionnaire (14-items) and the European Cyberbullying Intervention Project Questionnaire (22-items), both of Likert nature that respectively evaluate behaviors related to bullying and cyber-bullying. Higher values are indicative of worse outcomes.
Time frame: through study completion, an average of 1 year
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