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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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45/100
Our results show that DICE-net achieved an accuracy of 83.28% in the AD-CN problem using Leave-One-Subject-Out validation, outperforming several baseline models, and achieving good generalization performance.
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The following intersection over union values were obtained for the various slices in the verification dataset: frontal lobe axial/sagittal: 0.733/0.804; posterior cingulate cortex and precuneus coronal/sagittal: 0.661/0.726; lateral temporal lobe axial/coronal: 0.864/0.892; parietal lobe axial/coronal: 0.542/0.759; and striatum axial/sagittal: 0.679/0.752.
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Artificial intelligence tools are being rapidly integrated into clinical environments and may soon be incorporated into dementia diagnostic paradigms.
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Previous studies have explored the associations of retinal vessel calibre, measured from retinal photographs or fundus images using semi-automated computer programs, with cognitive impairment and dementia, supporting the concept that retinal blood vessels reflect microvascular changes in the brain.
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Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer's disease (AD) is the most common.
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Cognitive reserve (CR) has been introduced to explain individual differences in susceptibility to cognitive or functional impairment in the presence of age or pathology.
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41/100
Repositioning or repurposing drugs account for a substantial part of entering approval pipeline drugs, which indicates that drug repositioning has huge market potential and value.
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Brain aging is accompanied by patterns of functional and structural change.
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Accumulation of beta-amyloid in the brain and cognitive decline are considered hallmarks of Alzheimer's disease.
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Significantly stronger between-tracer correlations (P < .001) were observed after harmonization for both global amyloid burden indices and voxel-wise measurements in the training cohort and the external testing cohort.
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Differences in expressing facial emotions are broadly observed in people with cognitive impairment.
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Artificial intelligence can assist providers in a variety of patient care and intelligent health systems.
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Through this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD.
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48/100
Reduced speed and reduced smoothness in speed and pressure were observed particularly in the LBD group, while increased pauses and total durations were observed in both the AD and LBD groups.
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To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies.
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In silico reuse of old drugs (also known as drug repositioning) to treat common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked drugs, with potentially lower overall development costs and shorter development timelines.
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In this study, a drug repositioning algorithm based on a deep autoencoder and adaptive fusion was proposed to mitigate the problems of decreased precision and low-efficiency multisource data fusion caused by data sparseness.
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45/100
Older individuals infected by Human Immunodeficiency Virus (HIV) are at risk for developing HIV-Associated Neurocognitive Disorder (HAND), i.e., from reduced cognitive functioning similar to HIV-negative individuals with Mild Cognitive Impairment (MCI) or to Alzheimer's Disease (AD) if more severely affected.
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Drug repurposing aims to find new uses for already existing and approved drugs.
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Combining multi-modality data for brain disease diagnosis such as Alzheimer's disease (AD) commonly leads to improved performance than those using a single modality.
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With the approval of aducanumab on the “Accelerated Approval Pathway” and the recognition of amyloid load as a surrogate marker, new successful therapeutic approaches will be driven by combination therapy as was the case in oncology after the launch of immune checkpoint inhibitors.
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Our results revealed machine learning with novel biomarkers and multiple variables may increase the sensitivity and specificity in diagnosis of AD.
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Using pre-trained language models can improve AD prediction.
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Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results.
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Most current Alzheimer's disease (AD) and mild cognitive disorders (MCI) studies use single data modality to make predictions such as AD stages.
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The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities.
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45/100
Alzheimer's Disease (AD) is the most common cause of dementia globally.
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This work demonstrates a mobile version of VisMET capable of estimating the presence of cognitive impairment.
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There were significant positive correlations between the predicted MMSE scores and ground truth scores in the Patient and Healthy groups (r = 0.66, p < 0.001).
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57/100
A deep learning framework combining 3D CNN and LRP algorithms can be used with tau PET images to identify informative features for AD classification and may have application for early detection during prodromal stages of AD.
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284 eyes of 159 subjects (222 eyes from 123 cognitively healthy subjects and 62 eyes from 36 subjects with AD) were used to develop the model.
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The proposed hi-GCN method performs the graph embedding learning from a hierarchical perspective while considering the structure in individual brain network and the subject's correlation in the global population network, which can capture the most essential embedding features to improve the classification performance of disease diagnosis.
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When validated with subjects diagnosed using an official test, the model achieves COVID-19 sensitivity of 98.5% with a specificity of 94.2% (AUC: 0.97).
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Inter-reader agreements for the visual reading based on a 3-point scale (BAPL score) calculated by Fleiss kappa coefficients were 0.46 and 0.76 for the visual reading without and with the deep learning system, respectively.
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This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care-related AI research and possible practice implications.
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57/100
Deriving disease subtypes from electronic health records (EHRs) can guide next-generation personalized medicine.
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In recent years, electroencephalography (EEG) has emerged as a low-cost, accessible and objective tools for the early diagnosis of Alzheimer's disease (AD).
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Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases.
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The result revealed an accuracy rate of 0.84 ± 0.05, 0.79 ± 0.04, and 0.62 ± 0.06, respectively, for classifying AD vs. These results suggest that as a data-driven method, the combined CNN and EL approach can locate the most discriminable brain regions indicated by the trained ensemble model while the generalization ability of the ensemble model was maximized to successfully capture AD-related brain variations early in the disease process; it can also provide new insights into understanding the complex heterogeneity of whole-brain MRI changes in AD.
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Moreover, we found that more than half of the surveyed papers may have suffered from data leakage and thus reported biased performance.
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55/100
Despite subjects with Dominantly-Inherited Alzheimer's Disease (DIAD) represent less than 1% of all Alzheimer's Disease (AD) cases, the Dominantly Inherited Alzheimer Network (DIAN) initiative constitutes a strong impact in the understanding of AD disease course with special emphasis on the presyptomatic disease phase.
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Recently, deep-learning-based approaches have been proposed for the classification of neuroimaging data related to Alzheimer's disease (AD), and significant progress has been made.
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In this study, we developed a network-based deep-learning approach, termed deepDR, for in silico drug repurposing by integrating 10 networks: one drug-disease, one drug-side-effect, one drug-target and seven drug-drug networks.
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59/100
Alzheimer's Disease (AD) is one of the leading causes of death in developed countries.
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39/100
Brain age prediction is a machine learning method that estimates an individual's chronological age from their neuroimaging scans.
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The level of prediction error in the brain age estimation frameworks is associated with the authenticity of statistical inference on the basis of regression models.
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Deep learning, a state-of-the-art machine learning approach, has shown outstanding performance over traditional machine learning in identifying intricate structures in complex high-dimensional data, especially in the domain of computer vision.
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Deep neural networks have led to state-of-the-art results in many medical imaging tasks including Alzheimer's disease (AD) detection based on structural magnetic resonance imaging (MRI) data.
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Using AI for dementia diagnosis is still in research stage, however some of the work points to the development of new medical devices.
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Alzheimer's disease (AD) is one of the most common neurodegenerative diseases.
From across this tag
6 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 6. The same 6 appear here every time.
- Retinal image analytics detects white matter hyperintensities in healthy adults
- Integrating Comorbidity Knowledge for Alzheimer's Disease Drug Repurposing using Multi-task Graph Neural Network
- Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review
- Artificial Intelligence in Alzheimer’s Disease: Evidence Maturity and Clinical Translation Across the Care Pathway
- Automated quantification of cerebral microbleeds for ARIA-H monitoring in Aging and Alzheimer’s Disease: A multicenter deep learning validation
- SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry