YOGYAKARTA – A research team from Universitas Gadjah Mada (UGM), consisting of Samudera Fadlilla Jamaluddin, Riangga Novrianto, and Muhammad Oriza Nurfajri, is developing an innovative framework for early depression detection. This cross-faculty collaborative research leverages artificial intelligence (AI), including Large Language Models (LLM) and computer vision, to enable multimodal depression screening. The project, funded by industry/private partners, is expected to address the limitations of conventional detection methods that are time-consuming and resource-intensive.
Depression is a mental health disorder with high global prevalence, significantly impacting quality of life, productivity, and increasing the risk of morbidity and mortality. Early detection is crucial; however, conventional methods such as face-to-face clinical interviews and standardized questionnaires are often constrained by time, cost, and limited professional resources.
To address these challenges, advancements in artificial intelligence (AI) offer new potential. This ongoing research proposes a depression detection framework that integrates three main components to deliver more accurate and reliable results compared to single-method approaches.
The three components include:
- Facial expression analysis based on Vision Transformer: This technology enables the system to analyze and interpret facial expressions, which often serve as key indicators of emotional states.
- Exploratory open-ended interview based on LLM: By leveraging Large Language Models, the system can conduct open-ended interviews and analyze linguistic responses to detect patterns associated with depression.
- Patient Health Questionnaire (PHQ-9): This standard questionnaire is integrated to provide clinically validated quantitative assessment.
The integration of visual, linguistic, and quantitative data enables automated, efficient, and scalable depression screening. This research, which began in January, is a cross-faculty collaborative effort at Universitas Gadjah Mada and is supported by funding from the industry/private sector.
With the potential to provide more accessible and efficient screening tools, this AI-based depression detection system is expected to become a significant breakthrough in mental health care. Its successful implementation could support early identification of depression cases, enable faster intervention, and ultimately improve the quality of life for many individuals.
