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Depression and anxiety characterization and detection with multimodal deep learning

Nature Mental Health, Published online: 04 May 2026; doi:10.1038/s44220-026-00632-6

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.

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Posted in: Journal Article Abstracts on 09/12/2026 | Link to this post on IFP |
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