Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review
Article
Article Title | Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review |
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ERA Journal ID | 3179 |
Article Category | Article |
Authors | Barua, Prabal Datta, Jahmunah, Jahmunah, Oh, Oh Shu, Palmer, Elizabeth Emma, Yamakawa, Toshitaka, Kobayashi, Makiko and Acharya, Udyavara Rajendra |
Journal Title | Cognitive Neurodynamics |
Journal Citation | 18 (1), pp. 1-22 |
Number of Pages | 22 |
Year | 2024 |
Publisher | Springer |
Place of Publication | Netherlands |
ISSN | 1871-4080 |
1871-4099 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s11571-022-09904-0 |
Web Address (URL) | https://link.springer.com/article/10.1007/s11571-022-09904-0 |
Abstract | Epidemiological studies report high levels of anxiety and depression amongst adolescents. These psychiatric conditions and complex interplays of biological, social and environmental factors are important risk factors for suicidal behaviours and suicide, which show a peak in late adolescence and early adulthood. Although deaths by suicide have fallen globally in recent years, suicide deaths are increasing in some countries, such as the US. Suicide prevention is a challenging global public health problem. Currently, there aren’t any validated clinical biomarkers for suicidal diagnosis, and traditional methods exhibit limitations. Artificial intelligence (AI) is budding in many fields, including in the diagnosis of medical conditions. This review paper summarizes recent studies (past 8 years) that employed AI tools for the automated detection of depression and/or anxiety disorder and discusses the limitations and effects of some modalities. The studies assert that AI tools produce promising results and could overcome the limitations of traditional diagnostic methods. Although using AI tools for suicidal ideation exhibits limitations, these are outweighed by the advantages. Thus, this review article also proposes extracting a fusion of features such as facial images, speech signals, and visual and clinical history features from deep models for the automated detection of depression and/or anxiety disorder in individuals, for future work. This may pave the way for the identification of individuals with suicidal thoughts. |
Keywords | Artificial intelligence ; Deep learning ; Depression ; Anxiety ; Suicidal ideation; Classifiers ; Machine learning |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Southern Queensland |
Ngee Ann Polytechnic, Singapore | |
University of New South Wales | |
Sydney Children's Hospital, Australia | |
Kumamoto University, Japan | |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan |
https://research.usq.edu.au/item/z01q5/artificial-intelligence-assisted-tools-for-the-detection-of-anxiety-and-depression-leading-to-suicidal-ideation-in-adolescents-a-review
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