Expresso-AI: Explainable Video-Based Deep Learning Models for Depression Diagnosis

Researchers present Expresso-AI, a framework for interpreting deep learning models trained on facial videos to diagnose depression severity. The approach combines explainability with improved predictive performance by analyzing facial regions and temporal expression patterns, addressing a critical gap in automated mental health diagnosis where current methods lack interpretability.

The development of interpretable AI systems for mental health diagnosis represents a meaningful intersection of machine learning advancement and clinical utility. Expresso-AI addresses a fundamental limitation in existing depression detection models: while automated approaches have improved incrementally, they function as black boxes, limiting their adoption by healthcare professionals who require transparent reasoning for treatment decisions. This framework achieves interpretability by fine-tuning convolutional neural networks pre-trained on action recognition datasets, then examining saliency maps to identify which facial regions and temporal expressions drive diagnostic predictions.

The research builds on growing recognition that AI credibility in healthcare depends on explainability. Previous single-frame approaches to visual depression diagnosis lacked temporal context and interpretability, leaving clinicians unable to validate model decisions. By incorporating video sequences and generating both visual and quantitative explanations, this work bridges the gap between model performance and clinical applicability.

The broader impact extends to mental health infrastructure globally. Depression diagnosis currently relies heavily on subjective clinical assessment and patient self-reporting, creating barriers to early intervention in resource-limited settings. An objective, interpretable diagnostic tool could accelerate identification of at-risk populations while maintaining the transparency required for professional oversight and treatment planning.

Looking forward, validation in clinical settings remains essential. The framework's success depends on whether healthcare providers trust its explanations and whether diagnostic accuracy translates across diverse populations. Potential expansion to other mental health conditions and integration with existing clinical workflows represents the next critical phase for realizing this technology's societal impact.

• →Expresso-AI improves depression diagnosis by combining interpretable deep learning with video-based facial analysis rather than single-frame approaches.
• →The framework generates both visual saliency maps and quantitative explanations, enabling healthcare professionals to understand model reasoning.
• →Fine-tuning action recognition networks on depression datasets enables temporal expression analysis across video sequences.
• →Interpretability addresses a critical adoption barrier in clinical AI, where transparency is essential for professional trust and treatment planning.
• →Enhanced predictive performance combined with explainability positions this approach for potential clinical validation and real-world deployment.