ReviewAlzheimer's research & therapy2022
Deep learning-based speech analysis for Alzheimer's disease detection: a literature review.
Review in Alzheimer's research & therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it, 89 citations in OpenAlex.
- Multimodal AI for Alzheimer Disease Diagnosis: Systematic Review of Datasets, Models, and Modalities.Journal of medical Internet research · 2026Pooled it
- Advances in gait research related to Alzheimer's disease.Frontiers in neurology · 2025Pooled it
- Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis: A Systematic Literature Review.Journal of Alzheimer's disease : JAD · 2024Pooled it
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.Biosensors · 2026Review
- Enhancing Early Detection of Alzheimer's Disease: An Ensemble Model for Multi-Domain Cognitive Assessment Using Voice and Video.Sensors (Basel, Switzerland) · 2026Article
- WATCH-SS: Developing a Trustworthy and Explainable Modular Framework for Detecting Cognitive Impairment from Spontaneous Speech.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Prediction of cognitive impairment through speech data analysis: A comparative evaluation of deep learning models.PloS one · 2026Article
- WATCH-SS: Developing a Trustworthy and Explainable Modular Framework for Detecting Cognitive Impairment from Spontaneous Speech.medRxiv : the preprint server for health sciences · 2025Article
- Leveraging Clinical Record Geolocation for Improved Alzheimer's Disease Diagnosis Using DMV Framework.Biomedicines · 2025Article
- Multimodal Siamese networks for dementia detection from speech in women.Scientific reports · 2025Article
- Alzheimer's disease digital biomarkers multidimensional landscape and AI model scoping review.NPJ digital medicine · 2025Article
- Obfuscation via pitch-shifting for balancing privacy and diagnostic utility in voice-based cognitive assessment.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Deep learning-based Alzheimer's disease detection using magnetic resonance imaging and gene expression data.PloS one · 2025Article
- Effective deep convolutional neural network with attention mechanism for Alzheimer disease classification.Frontiers in radiology · 2025Article
- The structure of spontaneous speech changes in Alzheimer's disease: Crosslingual evidence from English and Greek.PloS one · 2025Article
- Adversarial Text Generation using Large Language Models for Dementia Detection.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2024Article
- Analyzing Multimodal Features of Spontaneous Voice Assistant Commands for Mild Cognitive Impairment Detection.Interspeech · 2024Article
- Natural Language Processing Applied to Spontaneous Recall of Famous Faces Reveals Memory Dysfunction in Temporal Lobe Epilepsy Patients.bioRxiv : the preprint server for biology · 2024Article
- Performance Assessment of ChatGPT versus Bard in Detecting Alzheimer's Dementia.Diagnostics (Basel, Switzerland) · 2024Article
- Unveiling the sound of the cognitive status: Machine Learning-based speech analysis in the Alzheimer's disease spectrum.Alzheimer's research & therapy · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlzheimer's disease has become one of the most common neurodegenerative diseases worldwide, which seriously affects the health of the elderly. Early detection and intervention are the most effective prevention methods currently. Compared with traditional detection methods such as traditional scale tests, electroencephalograms, and magnetic resonance imaging, speech analysis is more convenient for automatic large-scale Alzheimer's disease detection and has attracted extensive attention from researchers. In particular, deep learning-based speech analysis and language processing techniques for Alzheimer's disease detection have been studied and achieved impressive results.
methodsTo integrate the latest research progresses, hundreds of relevant papers from ACM, DBLP, IEEE, PubMed, Scopus, Web of Science electronic databases, and other sources were retrieved. We used these keywords for paper search: (Alzheimer OR dementia OR cognitive impairment) AND (speech OR voice OR audio) AND (deep learning OR neural network).
conclusionsFifty-two papers were finally retained after screening. We reviewed and presented the speech databases, deep learning methods, and model performances of these studies. In the end, we pointed out the mainstreams and limitations in the current studies and provided a direction for future research.
Indexed as
Identifiers
What OpenQuestion holds
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.