ArticleFrontiers in aging neuroscience2022
Multimodal Deep Learning Models for Detecting Dementia From Speech and Transcripts.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 3 syntheses or guidelines pooled it, 53 citations in OpenAlex.
- Transformer-Based Deep Learning Approaches for Speech-Based Dementia Detection: A Systematic Review.IEEE journal of biomedical and health informatics · 2026Pooled it
- Meta analysis of the diagnostic efficacy of transformer-based multimodal fusion deep learning models in early Alzheimer's disease.Frontiers in neurology · 2025Pooled it
- Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review.Journal of Alzheimer's disease : JAD · 2024Pooled it
- Multimodal Dementia Prediction With Large Language Models: Cross-Attention Over Text, Audio, and Image.JMIR aging · 2026Article
- Enhancing Early Detection of Alzheimer's Disease: An Ensemble Model for Multi-Domain Cognitive Assessment Using Voice and Video.Sensors (Basel, Switzerland) · 2026Article
- SpeechCARE: dynamic multimodal modeling for cognitive screening in diverse linguistic and speech task contexts.NPJ digital medicine · 2025Article
- Multimodal Siamese networks for dementia detection from speech in women.Scientific reports · 2025Article
- Multimodal learning of clinically accessible tests to aid diagnosis of neurodegenerative disorders: a scoping review.Health information science and systems · 2023Article
- A scoping review of neurodegenerative manifestations in explainable digital phenotyping.NPJ Parkinson's disease · 2023Article
- Performance of machine learning algorithms for dementia assessment: impacts of language tasks, recording media, and modalities.BMC medical informatics and decision making · 2023Article
- A remote speech-based AI system to screen for early Alzheimer's disease via smartphones.Alzheimer's & dementia (Amsterdam, Netherlands) · 2022Article
- Article
- Review
- Natural language processing in Alzheimer's disease research: Systematic review of methods, data, and efficacy.Alzheimer's & dementia (Amsterdam, Netherlands)Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's dementia (AD) entails negative psychological, social, and economic consequences not only for the patients but also for their families, relatives, and society in general. Despite the significance of this phenomenon and the importance for an early diagnosis, there are still limitations. Specifically, the main limitation is pertinent to the way the modalities of speech and transcripts are combined in a single neural network. Existing research works add/concatenate the image and text representations, employ majority voting approaches or average the predictions after training many textual and speech models separately. To address these limitations, in this article we present some new methods to detect AD patients and predict the Mini-Mental State Examination (MMSE) scores in an end-to-end trainable manner consisting of a combination of BERT, Vision Transformer, Co-Attention, Multimodal Shifting Gate, and a variant of the self-attention mechanism. Specifically, we convert audio to Log-Mel spectrograms, their delta, and delta-delta (acceleration values). First, we pass each transcript and image through a BERT model and Vision Transformer, respectively, adding a co-attention layer at the top, which generates image and word attention simultaneously. Secondly, we propose an architecture, which integrates multimodal information to a BERT model
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.