Surveillance of Suicide Ideation in Adolescents (VISIA)

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.