Ignite Creation Date:
2024-05-06 @ 8:20 PM
Last Modification Date:
2024-10-26 @ 3:25 PM
Study NCT ID:
NCT06341634
Status:
ENROLLING_BY_INVITATION
Last Update Posted:
2024-04-02
First Post:
2023-11-20
Brief Title:
Surveillance of Suicide Ideation in Adolescents VISIA
Sponsor:
Servicio Gallego de Salud
Organization:
Servicio Gallego de Salud
Study Overview
Official Title:
Surveillance of Suicide Ideation in Adolescents VISIA
Status:
ENROLLING_BY_INVITATION
Status Verified Date:
2024-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
VISIA
Brief Summary:
The goal of this observational study is to validate an AI algorithms 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 algorithms ability to identify early signs of suicide risk in these distinct participant populations
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 algorithms ability to identify early signs of suicide risk in these distinct participant populations
Detailed Description:
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 eg 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 studys 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
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 eg 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 studys 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None
Secondary IDs
| Secondary ID | Type | Domain | Link |
|---|---|---|---|
| 3494130747-130747-28-521 | OTHER | Ministry of Science and Innovation | None |
| TED2021-130747B-C22 | OTHER_GRANT | None | None |