Development and Evaluation of the DMIND Questionnaire: Preparing for AI Integration into an Effective Depression Screening Tool

Solaphat  Hemrungrojn; Kittipoch Saengsai; Pasit Jakkrawankult; Chanyanart Kiattiporn-Opas; Kantapat Chaichareenon; Arisara Amrapala; Kulvara  Lapanan; Sarunya  Hengprapom; Narin Hiransuthikul; Titipat  Achakulvisut; Natawut  Nupairoj; Phanupong  Phutrakool; Rapinpat  Yodlorchai; Peerapol  Vateekul

doi:10.33192/smj.v76i9.269527

Authors

Solaphat Hemrungrojn Center of Excellence in Digital and AI for Mental Health, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand; Cognitive Fitness and Biopsychiatry Technology Research Unit, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Kittipoch Saengsai Center of Excellence in Digital and AI for Mental Health, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand.
Pasit Jakkrawankult Center of Excellence in Digital and AI for Mental Health, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand.
Chanyanart Kiattiporn-Opas Center of Excellence in Digital and AI for Mental Health, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand.
Kantapat Chaichareenon Center of Excellence in Digital and AI for Mental Health, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand.
Arisara Amrapala Center of Excellence in Digital and AI for Mental Health, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand; Cognitive Fitness and Biopsychiatry Technology Research Unit, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Kulvara Lapanan Neuroscience Research Australia, Randwick, New South Wales, Australia; School of Population Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
Sarunya Hengprapom Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Narin Hiransuthikul Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Titipat Achakulvisut Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Bangkok, Thailand.
Natawut Nupairoj Center of Excellence in Digital and AI for Mental Health, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand; Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand.
Phanupong Phutrakool Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; Chula Data Management Center, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; Center of Excellence in Preventive and Integrative Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Rapinpat Yodlorchai Center of Excellence in Digital and AI for Mental Health, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand.
Peerapol Vateekul Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand.

DOI:

https://doi.org/10.33192/smj.v76i9.269527

Keywords:

Depression, Depression Screening, Mental Health, Artificial Intelligence

Abstract

Objective: Thailand’s mental health crisis is exacerbated by high demand and a shortage of mental health professionals. The research objective was to develop and validate the Detection and Monitoring Intelligence Network for Depression (DMIND) questionnaire, designed to be culturally relevant and easily administered in clinical settings. Crafted with expert input, items specifically conducive to artificial intelligence (AI) analysis were selected to facilitate the future development of an AI-assisted depression scoring model. This approach underscores the tool’s dual utility in both human-led and technology-enhanced diagnostics.

Materials and Methods: We enrolled 81 participants from psychiatric and tertiary care hospitals in Bangkok. Participants were assessed using the DMIND questionnaire, followed by the Hamilton Depression Rating Scale (HDRS-17). Statistical analyses included the content validity index (CVI), Cronbach’s alpha, Pearson’s correlation coefficient, Cohen’s kappa, and receiver operating characteristic (ROC) analysis. The Liu method, Youden index, and nearest neighbor method were used to determine the optimal cut-off point

Results: The DMIND questionnaire showed strong validity, with an item-level CVI (I-CVI) and scale-level CVI (S-CVI) exceeding 1.0, indicating strong consensus on its relevance and utility. The tool also demonstrated high internal consistency (Cronbach’s alpha = 0.96). ROC analysis showed an AUC of 0.88, indicating high accuracy in depression screening. An optimal cut-off score of 11.5 was identified, balancing predictive value and sensitivity.

Conclusion: The DMIND questionnaire represents a significant advancement in innovative mental health diagnostics, addressing unmet clinical needs by providing accurate and efficient assessments capable of AI integration for further enhancing mental health service delivery in Thailand.

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