Al-Driven Detection and Analysis of Mental Disorders: An Integrative Review of Machine Learning and Deep Learning Methodologies

DOI

10.2991/978-94-6463-823-3_59How to use a DOI?

Keywords

machine learning; deep learning; autism spectrum disorder; depressive disorder; bipolar disorder

Abstract

In recent years, escalating life stressors stemming from academic, professional, and interpersonal relationships have exerted significant psychological impacts on individuals. Compounded by the global COVID-19 pandemic since 2020, the prevalence of mental disorders has surged dramatically worldwide. Empirical data indicate that the worldwide incidence of major depressive disorder escalated by 28% in 2020, underscoring the profound repercussions of these compounding psychosocial challenges on global mental health. Concurrently with rapid global technological advancements, the utilization of artificial intelligence (AI) for analyzing individuals at risk of mental disorders has become imperative in the current societal context, facilitating early detection and intervention. This review synthesizes research on autism spectrum disorder, major depressive disorder, and bipolar disorder, aiming to provide foundational knowledge for newcomers to the field. Specifically, it addresses: Behavioral anomalies exhibited by patients with these conditions; Methodologies for integrating symptom manifestations with machine learning (ML) and deep learning (DL) models to enhance screening accuracy; A systematic summary of existing studies, including both foundational and innovative computational frameworks in this domain. The paper further identifies current research limitations and proposes potential trajectories for advancing AI-assisted mental health screening systems.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rujing Fu
AU  - Shijin Zhao
AU  - Jiawei Zhu
PY  - 2025
DA  - 2025/08/31
TI  - Al-Driven Detection and Analysis of Mental Disorders: An Integrative Review of Machine Learning and Deep Learning Methodologies
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 587
EP  - 602
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_59
DO  - 10.2991/978-94-6463-823-3_59
ID  - Fu2025
ER  -