Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)

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Multimodal AI: A Step Towards Objective Depression Diagnosis

Authors
Mohammed Anas Kapadia1, *, Muskan Patel2, Muhammad Ismail Shaikh2, Soban Maruf2, Safia Sadruddin2
1Department of Computer Engineering, Anjuman-I-Islam’s Kalsekar Technical Campus, Navi-Mumbai, New Panvel, 410206, Maharashtra, India
2Department of Computer Engineering, Anjuman-I-Islam’s Kalsekar Technical Campus, Navi-Mumbai, New Panvel, 410206, Maharashtra, India
*Corresponding author. Email: anaskapadia101@gmail.com
Corresponding Author
Mohammed Anas Kapadia
Available Online 7 October 2025.
DOI
10.2991/978-94-6463-852-3_28How to use a DOI?
Keywords
Mental Health; Depression; CNN; Speech; FER; RAVDESS
Abstract

Depression, a widespread and debilitating mental health condition, often remains undiagnosed due to the subjective and time-intensive nature of traditional diagnostic methods. This study presents an artificial intelligence system that combines different modalities to detect depression because the current clinical diagnosis is based too heavily on subjective methods. This system combines facial recognition analysis with speech recognition that uses LSTM networks and questionnaire-based evaluations and achieves an accuracy 80%. A late fusion design among these modalities enables the framework to perform better than single input techniques. These diverse datasets include the Face Expression Recognition Dataset, TESS and RAVDESS along with the Patient Health Questionnaire-9 (PHQ-9), helping the system provide a strong scalable solution for depression evaluation and mental health screening that leads to timely intervention opportunities.

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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Volume Title
Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)
Series
Advances in Intelligent Systems Research
Publication Date
7 October 2025
ISBN
978-94-6463-852-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-852-3_28How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - Mohammed Anas Kapadia
AU  - Muskan Patel
AU  - Muhammad Ismail Shaikh
AU  - Soban Maruf
AU  - Safia Sadruddin
PY  - 2025
DA  - 2025/10/07
TI  - Multimodal AI: A Step Towards Objective Depression Diagnosis
BT  - Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)
PB  - Atlantis Press
SP  - 447
EP  - 461
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-852-3_28
DO  - 10.2991/978-94-6463-852-3_28
ID  - Kapadia2025
ER  -