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deep
205 studies tagged deep, dated 2006-12-06 to 2026-10-30. Bibliography: BibTeX (.bib) · RIS (.ris)
Newest in this topic
- Tau-CTFP: A connectome-informed continuous-time first-passage model for forecasting the onset of regional tau PET abnormality 2026-10-30
- A Wasserstein Weisfeiler-Lehman Kernel Framework for Multimodal Brain Connectome Analysis 2026-10-29
- Current gaps in downstream applications of fMRI foundation models: lessons learned from BrainLM & BrainHarmonix on Prevent-AD 2026-10-26
Studies
205 studies carry this tag.
Date range
2006 to 2026
Top journals
- Research Square 22
- medRxiv 22
- NeuroImage 14
- bioRxiv 14
- Preprints.org 7
87 journals in all.
Newest first, then by address. Each study has a usefulness score out of 100 (rubric version 4.0); tap a score to see what makes it up. Tags were assigned automatically from each study's title and summary. The same studies as a filter in the library.
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Multimodal integration of cognitive, neuroimaging, and polygenic data in the MHS-RWD tool yields strong discrimination for both early diagnosis of dementia and AD differential diagnosis.
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All three frameworks recognized biologically coherent AD-related signal despite differences in training cohort and assay methodology.
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The present work builds upon progress in foundation artificial intelligence (AI) models for thepurpose of supporting the Alzheimer’s Disease Neuroimaging Initiative (ADNI).
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Artificial intelligence (AI) is now a credible translational research platform across the Alzheimer’s disease (AD) pathway, but its clinical value is often difficult to judge from performance metrics alone.
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Activities of daily living (ADLs) provide important indicators of functional decline in people living with dementia, motivating the need for continuous in-home monitoring.
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Early, scalable detection of Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains a global health priority, but non-invasive biomarkers that capture network-level dysfunction during standardized cognitive challenge remain limited.
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Event-based models (EBMs) are used to infer ordering of biomarker alteration patterns with respect to disease progression.
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This study contributes to the healthcare field by demonstrating the potential of providing a fast, transparent, and affordable assessment system that is accessible to low-income populations and clinics with limited budget.
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Recent research has revealed that linguistic features are effective digital markers for the early detection of Alzheimer’s disease (AD).
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EcoXAI replaces monolithic AI text interfaces with an autonomous execution-driven framework with specialized bioinformatics agents for delivering proactive, data-driven insights grounded in established biological knowledge.
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Parkinson’s disease (PD) progression is highly heterogeneous.
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Among 11,377 AD hospitalizations, 600 in-hospital deaths occurred (5.27%).
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MCH-Guard offers a flexible clinical decision-support tool for optimizing spontaneous MCH & ARIA surveillance.
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We trained a self-configuring nnU-Net model for CMB segmentation in a hetero-geneous multicenter sample ( n =264), including 1.5T and 3T field strengths, SWI and T2*-GRE sequences, and community and clinical cohorts.
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A bstract INTRODUCTION Treatment response in Alzheimer’s disease (AD) varies substantially across patients, yet no validated frameworks exist to estimate heterogeneous treatment effects (HTE) from observational data while controlling for confounding bias.
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Spontaneous speech acoustics capture p-tau217/APOE-defined AD risk despite preserved cognition, supporting speech as a scalable, biologically grounded biomarker for preclinical AD risk stratification.
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The analytic sample included 4,465 individuals (mean age 69.7±10.4 years; 58.7% female; 75.8% non-Hispanic White).
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Alzheimer’s disease (AD) is increasing in prevalence, and early detection is essential for timely care.
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Large language models (LLMs) offer new opportunities to synthesize the vast and heterogeneous biomedical literature, yet their potential to support drug target prioritization in complex diseases such as Alzheimer’s disease (AD) remains largely unexplored.
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Semi-supervised multimodal fusion improves prediction and reveals interpretable imaging patterns that predict APOE4 and clinical diagnoses better than traditional approaches.
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Mild cognitive impairment (MCI) is a prodromal stage of dementia, and its early detection is critical for improving clinical outcomes.
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Cross-modality translation between MRI and PET imaging is challenging due to the distinct mechanisms underlying these modalities.
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The Personalized Multi-Domain Digital Platform for Dementia Prevention (PM(DP)*2) is a conceptualized evidence-based initiative designed to reduce dementia burden through tailored interventions.
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46/100
Models including plasma biomarkers yielded the most accurate predictions of tau-PET burden (best model: R-squared = 0.66-0.69), with especially high contribution from plasma phosphorylated tau-217 (p-tau217).
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Alzheimer’s disease is the fifth-leading cause of death for adults over the age of 65.
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Alzheimer's disease is the most common cause of dementia in the elderly population (aged 65 years and over), followed by vascular dementia, Lewy body dementia, and rare types of neurodegenerative diseases, including frontotemporal dementia.
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Alzheimer's disease (AD) is a global healthcare challenge lacking a simple and affordable detection method.
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71/100
Alzheimer's disease is a common brain disorder affecting many people worldwide.
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The self-supervised ADRET model had superior accuracy when compared with ADVAS, in both UK Biobank (98.27% vs 77.20%; P<.001) and our institutional testing data sets (98.90% vs 94.17%; P=.04).
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This study used measured data from 108 participants and the synthesized data generated from the measured data.
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59/100
DL-ADprob was independently prognostic in both ADNI-MCI (P < .001) and HABS-CU (P = .048) sets.
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In total, 371 eyes of 249 control subjects and 75 eyes of 52 PD subjects were used for training, validation, and testing.
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The importance of early Alzheimer's Disease screening is becoming more apparent, given the fact that there is no way to revert the patient's status after the onset.
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This paper introduces a new deep learning-assisted offline smartphone platform for early AD screening, offering rapid disease detection in low-resource areas.
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Explainable artificial intelligence (XAI) has gained much interest in recent years for its ability to explain the complex decision-making process of machine learning (ML) and deep learning (DL) models.
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Alzheimer's Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia.
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The drug development pipeline for a new compound can last 10-20 years and cost over $10 billion.
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Recent research has linked systemic metabolic disorders to cognitive decline and dementia risk, including Alzheimer's. This is suspected to be due to lifestyle-related vascular impairments from atherosclerosis and other factors, such as malnutrition and anaemia.
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Digital biomarkers are of growing interest in the field of Alzheimer's Disease (AD) research.
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Animal studies show aging varies between individuals as well as between organs within an individual 1-4 , but whether this is true in humans and its effect on age-related diseases is unknown.
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Retinal Optical Coherence Tomography (OCT) allows the non-invasive direct observation of the central nervous system, enabling the measurement and extraction of biomarkers from neural tissue that can be helpful in the assessment of ocular, systemic and Neurological Disorders (ND).
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We obtained a predictive accuracy of 0.71 (95 % CI [0.59, 0.84]) with an AUC of 0.69 in the HRS test dataset and got an accuracy of 0.62 (95 % CI [0.56, 0.68]) with an AUC of 0.63 in the ROSMAP dataset.
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As the population ages, neurodegenerative diseases are becoming more prevalent, making it crucial to comprehend the underlying disease mechanisms and identify biomarkers to allow for early diagnosis and effective screening for clinical trials.
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Artificial Intelligence is a rapidly expanding field within computer science that encompasses the emulation of human intelligence by machines.
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The use of EHRs for AD-related research is promising, and AD-BERT shows superior predictive performance in modeling MCI-to-AD progression prediction.
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Alzheimer's Disease (AD) is a multifactorial disease that shares common etiologies with its multiple comorbidities, especially vascular diseases.
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Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in hospitals across the world.
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Brain MR images are the most suitable method for detecting chronic nerve diseases such as brain tumors, strokes, dementia, and multiple sclerosis.
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41/100
Alzheimer's disease (AD) remains a global health challenge in the 21st century due to its increasing prevalence as the major cause of dementia.
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These preliminary findings suggest that derived digital biomarkers may identify cognitive impairment in preclinical and prodromal AD, and may predict disease progression.
From across this tag
10 of the 205 studies in this tag, picked by a fixed rule (a hash of each study's address), not by score or date; each page of this list shows a different 10. The same 10 appear here every time.
- EEG Pathology Detection via Clinical Concept Bottleneck Models
- Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging
- Modeling autosomal dominant Alzheimer's disease with machine learning
- Multimodal Transformer-Based Fusion of Blood-Based and Digital Biomarkers for Explainable (XAI) Ultra-Early Alzheimer's Disease Detection
- Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer's Disease Prediction From Mild Cognitive Impairment
- Structural neuroimaging as clinical predictor: A review of machine learning applications
- Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects
- Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer's Disease Diagnosis
- Drug repositioning based on the heterogeneous information fusion graph convolutional network
- Evaluating biomarkers and prediction models: predictive utility analysis with E2P Simulator