An Analysis of Artificial Intelligence and Computer Vision Applications in the Identification of Signs of Depression
Abstract
The proposed article seeks to analyze and highlight concepts related to Computer Vision, one of the study areas of Artificial Intelligence, focused on diagnosing symptoms related to depression and how we can use this technique to solve related problems. The adopted methodology consisted of a literature review conducted using the Google Scholar platform. The selection criteria included the presence of keywords such as “Artificial Intelligence,” “Computer Vision,” “Emotions,” “Depression,” and the use of AI in the field of mental health.
References
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Carvalho N, Laurent E, Noiret N, et al. Eye movement in unipolar and bipolar depression: A systematic review of the literature. Front Psychol 2015;6:1809. DOI: 10.3389/fpsyg.2015.01809
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Hamilton, J. P., Chen, M. C., Waugh, C. E., Joormann, J., and Gotlib, I. H. (2015). Distinctive and common neural underpinnings of major depression, social anxiety, and their comorbidity. Social Cognitive and Affective Neuroscience, 10(4), 552-560.
Júlio, J. G. (2024). Inteligência Artificial e Depressão: revisão sistemática. Revista Da UI_IPSantarém, 12(1), e33936. DOI: 10.25746/ruiips.v12.i1.33936
Kleinsmith, A., & Bianchi-Berthouze, N. (2013). Affective body expression perception and recognition: A survey. IEEE Transactions on Affective Computing, 4(1), 15-33.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Li, S. e Deng, W. (2020). Deep Facial Expression Recognition: A Survey. IEEE Transactions on Affective Computing, 2020.
Low, L. S. A., Maddage, N. C., Lech, M., Sheeber, L. B., and Allen, N. B. (2010). Detection of clinical depression in adolescents’ speech during family interactions. IEEE Transactions on Biomedical Engineering, 58(3), 574-586.
Machado, D. S., & Viana, E. A. A Inteligência Artificial E Seu Impacto Na Saúde: Desafios E Perspectivas. Anais Do Simpósio Brasileiro De Inteligência Artificial, 17.
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Noyes B, Biorac A, Vazquez G, Khalid-Khan S, Munoz D, Booij L. (2023). Eye-tracking in adult depression: protocol for a systematic review and meta-analysis. BMJ Open. 2023 Jun 6;13(6):e069256. DOI: 10.1136/bmjopen-2022-069256. PMID: 37280037; PMCID: PMC10254607.
RUSSEL, S., & NORVIG, P. (2013). Inteligencia Artificial. São Paulo – SP: Campus.
Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall.
Scherer, S., Stratou, G., & Morency, L. P. (2013). Audiovisual behavior descriptors for depression assessment. In Proceedings of the 15th ACM on International conference on multimodal interaction (pp. 135-140).
Wang, J. (2004). A longitudinal population-based study of treated and untreated major depression.MedicalCare, 42(6), 543-550. DOI: 10.1097/01.mlr.0000128001.73998.5c
Zhang, J., Yin, Z., Chen, P., & Nichele, S. (2020). Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review. Information Fusion, 59, 103-126.
Zucolotto, T. E.; Gerônimo, R. M. P.; Silva, P. I. J.; Da Costa, L. C. S. A inteligência artificial na medicina: aplicações atuais e potenciais. Brazilian Journal of Health Review, [S. l.], v. 6, n. 6, p. 31237–31247, 2023. DOI: 10.34119/bjhrv6n6-358. Disponível em: [link]. Acesso em: 6 may. 2025.
American Psychiatric Association.(2023).Manual Diagnóstico e Estatístico de Transtornos Mentais (DSM-5-TR). Artmed.
Baltrušaitis, T., Ahuja, C., & Morency, L. P. (2018). Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 423-443.
Barbosa, S. D. J. e da Silva, B. S. (2010) “Interação Humano-Computador”, Elsevier Brasil.
Bevan, N. (2009). “Extending quality in use to provide a framework for usability measurement”. In Human Centered Design: First International Conference, HCD 2009, Held as Part of HCI International 2009, San Diego, CA, USA, July 19-24, 2009 Proceedings 1 (pp. 13-22). Springer Berlin Heidelberg.
Braga, A. V., Lins, A. F., Soares, L. S., Fleury, L. G., Carvalho, J. C., & Prado, R. S. do. (2019). Machine learning: O Uso da Inteligência Artificial na Medicina/ Machine learning: The Use of Artificial Intelligence in Medicine. Brazilian Journal of Development, 5(9), 16407–16413. DOI: 10.34117/bjdv5n9-190
Calvo, R. A., Milne, D. N., Hussain, M. S., and Christensen, H. (2017). Natural language processing in mental health applications using non-clinical texts. Natural Language Engineering, 23(5), 649-685.
Carvalho N, Laurent E, Noiret N, et al. Eye movement in unipolar and bipolar depression: A systematic review of the literature. Front Psychol 2015;6:1809. DOI: 10.3389/fpsyg.2015.01809
Elvas, L. B., & Ferreira, J. (2023). Aplicações da IA na saúde. In F. Camacho (Eds.). 88 vozes pela inteligência artificial: O que fica para a máquina e o que fica para o homem? (pp. 400-419).
Ferreira, B. D., & Prinz, R. C. Desafios E Perspectivas Da Implantação De Inteligência Artificial Na Assistência Médica. Anais Do Simpósio Brasileiro De Inteligência Artificial, p. 31.
Hamilton, J. P., Chen, M. C., Waugh, C. E., Joormann, J., and Gotlib, I. H. (2015). Distinctive and common neural underpinnings of major depression, social anxiety, and their comorbidity. Social Cognitive and Affective Neuroscience, 10(4), 552-560.
Júlio, J. G. (2024). Inteligência Artificial e Depressão: revisão sistemática. Revista Da UI_IPSantarém, 12(1), e33936. DOI: 10.25746/ruiips.v12.i1.33936
Kleinsmith, A., & Bianchi-Berthouze, N. (2013). Affective body expression perception and recognition: A survey. IEEE Transactions on Affective Computing, 4(1), 15-33.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Li, S. e Deng, W. (2020). Deep Facial Expression Recognition: A Survey. IEEE Transactions on Affective Computing, 2020.
Low, L. S. A., Maddage, N. C., Lech, M., Sheeber, L. B., and Allen, N. B. (2010). Detection of clinical depression in adolescents’ speech during family interactions. IEEE Transactions on Biomedical Engineering, 58(3), 574-586.
Machado, D. S., & Viana, E. A. A Inteligência Artificial E Seu Impacto Na Saúde: Desafios E Perspectivas. Anais Do Simpósio Brasileiro De Inteligência Artificial, 17.
Milano, D. and Honorato, L. B. (2010) “Visão Computacional”, Faculdade de Tecnologia, Universidade Estadual de Campinas.
Noyes B, Biorac A, Vazquez G, Khalid-Khan S, Munoz D, Booij L. (2023). Eye-tracking in adult depression: protocol for a systematic review and meta-analysis. BMJ Open. 2023 Jun 6;13(6):e069256. DOI: 10.1136/bmjopen-2022-069256. PMID: 37280037; PMCID: PMC10254607.
RUSSEL, S., & NORVIG, P. (2013). Inteligencia Artificial. São Paulo – SP: Campus.
Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall.
Scherer, S., Stratou, G., & Morency, L. P. (2013). Audiovisual behavior descriptors for depression assessment. In Proceedings of the 15th ACM on International conference on multimodal interaction (pp. 135-140).
Wang, J. (2004). A longitudinal population-based study of treated and untreated major depression.MedicalCare, 42(6), 543-550. DOI: 10.1097/01.mlr.0000128001.73998.5c
Zhang, J., Yin, Z., Chen, P., & Nichele, S. (2020). Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review. Information Fusion, 59, 103-126.
Zucolotto, T. E.; Gerônimo, R. M. P.; Silva, P. I. J.; Da Costa, L. C. S. A inteligência artificial na medicina: aplicações atuais e potenciais. Brazilian Journal of Health Review, [S. l.], v. 6, n. 6, p. 31237–31247, 2023. DOI: 10.34119/bjhrv6n6-358. Disponível em: [link]. Acesso em: 6 may. 2025.
Published
2025-07-01
How to Cite
TAGUCHI, Gabriel I. O.; FREITAS, Matheus A. S. de; VIEIRA, Anacilia M. C. de A. P..
An Analysis of Artificial Intelligence and Computer Vision Applications in the Identification of Signs of Depression. In: ICET TECHNOLOGY CONFERENCE (CONNECTECH), 2. , 2025, Itacoatiara/AM.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 226-235.
DOI: https://doi.org/10.5753/connect.2025.11398.