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Early Depression Detection Using Modern AI Techniques: Issues, Opportunities, and Challenges
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Author(s): Sharmistha Dey (Galgotias University, India)and Krishan Veer Singh (Galgotias University, India)
Copyright: 2025
Pages: 20
Source title:
Exploring the Micro World of Robotics Through Insect Robots
Source Author(s)/Editor(s): U. Vignesh (Vellore Institute of Technology, Chennai, India), Annavarapu Chandra Sekhara Rao (Indian Institute of Technology (ISM), Dhanbad, India), Saleem Raja (University of Technology and Applied Sciences, Shinas, Oman)and P. Chitra (GITAM University, Bangalore, India)
DOI: 10.4018/979-8-3693-6150-4.ch006
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Abstract
Depression is a widespread and debilitating mental health disorder, impacting over 300 million individuals globally, as reported by the World Health Organization. Early detection and timely intervention are essential for effective treatment and mitigating the severity of depressive symptoms. However, accurately identifying the nuanced symptoms of depression—manifested through body language, speech patterns, or neurological signals—remains a significant challenge. The advent of modern AI technologies has revolutionized the landscape of depression detection, offering new methodologies for identifying these symptoms This study investigates the current challenges, opportunities, and advancements in AI-driven approaches to early depression detection. We conducted a comprehensive review of approximately 60 high-quality, peer-reviewed research articles from reputable journals and conferences, focusing on the relevance and objectives of each study. Our findings highlight the latest trends in depression detection and outline the obstacles faced in this field, providing a roadmap for future researchers aiming to enhance early detection strategies and improve mental health outcomes.
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