In this Review, the authors highlight the role of multimodal deep learning in diagnosing depression and anxiety, utilizing diverse data sources. Key methods include convolutional, recurrent and graph neural networks, addressing challenges in data fusion, feature extraction and model interpretability for enhanced clinical relevance.
- Tianchi Lu
- Ling Cho
- Ka-Chun Wong
