Observational studyAlzheimer's research & therapy2025
Digital twins and non-invasive recordings enable early diagnosis of Alzheimer's disease.
Observational study in Alzheimer's research & therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05569083 (PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer's Disease With Machine Learning), which is not on this map. Cited by 13 papers.
What it found
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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.
PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer's Disease With Machine Learning
Who cites it
13 citing papers in PubMed.
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.Biosensors · 2026Review
- Digital twin for neurological conditions: a systematic scoping review.Biomedical engineering letters · 2026Review
- Neural population models for EEG: From Canonical models to alternative model structures.PLoS computational biology · 2026Article
- Virtual brain and electroencephalography explain the variance of memory alterations in mild cognitive impairment.Alzheimer's research & therapy · 2026Article
- Participatory Digital Twins for Chronic Care: From Predictive Models to Shared Sensemaking.Journal of participatory medicine · 2026Article
- Deep Learning-Based Alzheimer's Disease Detection from Multi-Channel EEG Using Fused Time-Frequency Image Grids.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence-driven nano-enhanced stem cell therapy for neurodegenerative diseases: from rational design to clinical translation.Journal of nanobiotechnology · 2026Review
- Digital twins support cross-modal and cross-centric classification of mild cognitive impairment.Communications medicine · 2026Article
- Mapping the future of medicine through digital twins.Frontiers in molecular medicine · 2026Review
- Digital Twin Cognition: AI-Biomarker Integration in Biomimetic Neuropsychology.Biomimetics (Basel, Switzerland) · 2025Review
- Mitochondrial and ER stress crosstalk in TBI: mechanistic insights and therapeutic opportunities.Frontiers in cellular neuroscience · 2025Review
- Digital twins in healthcare: a comprehensive review and future directions.Frontiers in digital health · 2025Review
- Digital twins in dementia: Early evidence and future directions for precision psychiatry.Industrial psychiatry journalArticle
Corrections and comments
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Authors and funding
20 authors.
Funding
Abstract
backgroundThe diagnosis of Alzheimer's disease (AD) in its preclinical stages, such as subjective cognitive decline (SCD), is crucial for a timely management of the condition. However, current early diagnostic methods are unsuitable for preclinical screenings due to limited availability and diagnostic reliability. Additionally, reliance on invasive and scarcely available methods exacerbates the underdiagnosis of AD in its preclinical forms.
methodsWe introduce an early diagnostic pipeline based on the Digital Alzheimer's Disease Diagnosis (DADD) digital twin model, which derives personalized AD biomarkers from non-invasive electroencephalographic (EEG) recordings. These biomarkers reconstruct patient-specific neurodegeneration, capturing synaptic and connectivity degeneration mechanisms. Digital biomarkers were used to predict cerebrospinal fluid (CSF) biomarker positivity for AD and clinical conversions at follow-up in 124 participants with varying degrees of cognitive decline, including a control group of 19 healthy subjects.
resultsDigital biomarkers derived from the DADD model: i) Robustly distinguished SCD from healthy participants, improving classification accuracy by 7% compared to standard EEG biomarkers; ii) Identified patients positive for CSF biomarkers of AD with 88% accuracy (significantly outperforming standard EEG biomarkers, which achieved 58% accuracy); iii) Predicted follow-up conversions to clinical cognitive decline with 87% accuracy (compared to 54% accuracy for standard EEG biomarkers).
conclusionsThe DADD model provided robust digital AD biomarkers with strong diagnostic and prognostic value for preclinical AD, enabling the prediction of CSF biomarkers and clinical conversions using only non-invasive EEG recordings. This is particularly important as preclinical patients, such as those with SCD, are often excluded from diagnostic procedures like lumbar puncture. Predicting CSF biomarkers by combining digital twins with non-invasive recordings could revolutionize AD diagnosis in its early stages, paving the way for the clinical application of digital twins in AD diagnostics.
trial registrationClinical Trial identifier: NCT05569083 (submitted 2022-08-24).
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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.