Alzheimer's disease
PulseAugur coverage of Alzheimer's disease — every cluster mentioning Alzheimer's disease across labs, papers, and developer communities, ranked by signal.
- instance of Tau PET Imaging in the NACC Study Cohort 90%
- instance of ADNI-3 90%
- instance of Alzheimer's Disease Neuroimaging Initiative 70%
- used by Alzheimer's Disease Neuroimaging Initiative 70%
- developed by magnetic resonance imaging 70%
- used by magnetic resonance imaging 70%
- instance of Amyloid 70%
- used by Tau PET Imaging in the NACC Study Cohort 70%
- instance of Beta amyloid 70%
- instance of dementia 70%
- instance of frontotemporal dementia 70%
- used by mini–mental state examination 70%
- 2026-08-03 research_milestone Clinical trials for a surgical procedure to treat Alzheimer's disease are underway in Hong Kong, showing early promise. source
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New AI framework transfers MRI knowledge to speech for Alzheimer's screening
Researchers have developed MINT (Multimodal Imaging-to-Speech Knowledge Transfer), a novel framework designed for early Alzheimer's disease screening. This system transfers knowledge from structural MRI scans to speech …
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New XGML framework predicts Alzheimer's disease using brain graph analysis
Researchers have developed a novel explainable graph-theoretical machine learning (XGML) framework to predict Alzheimer's disease (AD) and related cognitive decline. This approach constructs individual metabolic brain g…
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New AI frameworks improve Alzheimer's diagnosis using MRI and clinical data
Researchers have developed new deep learning frameworks for diagnosing Alzheimer's disease using multimodal data. One study focuses on grounding image-based models with anatomical references and addressing label leakage…
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AI model adapts to Alzheimer's MRI tasks with minimal retraining
Researchers have developed a generalizable feature extractor for Alzheimer's disease-related brain MRI tasks, demonstrating the effectiveness of transfer learning in neuroimaging. By adapting a pre-trained 3D convolutio…
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LLMs used to report on retinal vascular phenotypes for Alzheimer's research
Researchers have developed a novel pipeline for analyzing retinal optical coherence tomography angiography (OCTA) images to aid in the early identification of Alzheimer's disease. This system integrates vessel segmentat…
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New AI model enhances brain age estimation using MRI and blood flow data
Researchers have developed a novel multimodal framework to enhance the accuracy of brain age estimation, a biomarker for neurobiological aging and disease risk. This new approach combines predictions from two distinct 3…
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LLM-powered agent system automates neuroscience analysis
Researchers have developed NS-Copilot, an LLM-driven agent system designed to autonomously handle neuroscience analysis workflows. This system integrates domain-specific pre-trained models and supports various neuroscie…
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Speech enhancement may hinder Alzheimer's detection models, study finds
A new research paper published on arXiv questions the effectiveness of speech enhancement and data curation techniques in Alzheimer's disease detection models. The study found that while "cleaner" speech datasets can im…
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China approves 'telepathy' devices, challenging Neuralink
China's National Medical Products Administration has approved several brain-computer interface (BCI) devices this year, signaling a significant push by domestic companies to lead in this emerging technology. These BCI p…
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New methods improve Alzheimer's detection via speech analysis, but data cleaning may hurt generalization
Researchers have developed a new method called LLM-Anchored Paralinguistic Enrichment (LAPE) to improve the detection of Alzheimer's disease using speech analysis. LAPE integrates linguistic content with paralinguistic …
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3D CNN accurately classifies Alzheimer's using MRI scans · arXiv research
Researchers have developed a multimodal 3D convolutional neural network (CNN) for classifying Alzheimer's disease (AD) using structural MRI data. The model integrates T1 structural information with gray matter, white ma…
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AI models advance Alzheimer's diagnosis using EEG data
Researchers have developed two novel approaches for diagnosing Alzheimer's disease using electroencephalography (EEG) data. One method, GraM-Diff, utilizes a Graph-Mamba diffusion framework to generate synthetic EEG dat…
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Alzheimer's detection advances with blood tests and proactive care
Early detection of Alzheimer's disease is crucial, as proactive care can prevent up to 40% of dementia cases. Persistent cognitive decline, not normal aging, signals biological changes in the brain that require timely s…
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New PANDA framework enhances multimodal medical prediction with incomplete data
Researchers have developed PANDA, a novel two-stage framework designed to enhance multimodal medical prediction models by effectively utilizing auxiliary data that is not available for all subjects. The framework learns…
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AI framework quantifies MRI and PET contribution in Alzheimer's diagnosis
Researchers have developed a new framework called the Modality Contribution Network (MCNet) and Modality Contribution Score (MCS) to quantify the diagnostic contribution of different imaging techniques in Alzheimer's di…
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90-year-old woman in Beijing cares for husband with Alzheimer's
A 90-year-old woman in Beijing, Wen Bo, is dedicated to caring for her husband who has been diagnosed with Alzheimer's disease. She prioritizes maintaining his dignity while managing his care, opting to be his full-time…
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FDA clears Alzheimer's blood test developed at WashU Medicine
The U.S. Food and Drug Administration (FDA) has cleared for marketing the PrecivityAD2 blood test, which aids in the evaluation of Alzheimer's disease. Developed by C2N Diagnostics, a startup company originating from Wa…
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New methods tackle within-class variation in Alzheimer's detection
Researchers have developed new methods to address the significant within-class variation in Alzheimer's disease detection using machine learning. The proposed approaches, Soft Target Distillation (SoTD) and Instance-lev…
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New Bayesian Learning Framework Tracks Alzheimer's Disease Progression
Researchers have developed a new framework called Disease Continuum Positioning (DCP) that uses longitudinal Bayesian learning to estimate the continuous progression of Alzheimer's disease from diffusion tensor imaging …
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AI designs intrabodies for cellular treatment of Alzheimer's, Parkinson's
Researchers have developed AI-designed intrabodies capable of functioning within human cells, offering potential new treatments for neurodegenerative diseases like Alzheimer's and Parkinson's. These novel molecules are …