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classification
842 studies tagged classification, dated 1983-08-01 to 2026-09-25. Bibliography: BibTeX (.bib) · RIS (.ris)
Newest in this topic
- Diagnostic accuracy of remote self-administered digital cognitive assessment by clinical diagnosis and Alzheimer's disease PET imaging biomarkers 2026-09-25
- Multi-scale 3D CNN with imbalance-aware training for structural MRI-based six-class Alzheimer disease staging 2026-09-22
- T1 mapping in Alzheimer’s disease: a quantitative MRI study with amyloid-PET correlation 2026-09-21
Studies
842 studies carry this tag.
Date range
1983 to 2026
Top journals
- Neurology 83
- Journal of nuclear medicine : official publication, Society of Nuclear Medicine 42
- Brain : a journal of neurology 34
- NeuroImage 31
- Alzheimer s & Dementia 30
283 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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Both patient groups showed significant deviations from HC, but with distinct patterns.
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ATTRACTnet was developed in an internal test set of 799 patients (mean [SD] age, 75.1 [11.1] years; 516 [64.7%] male and 283 [35.3%] female) using 5-fold cross-validation with an additional external test set of 422 patients.
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The Neural Network classifier achieved an accuracy of 91% with a 95% CI ranging from 0.79-0.97 and an AUC of 94%, on the test set after identifying the most significant features for AD prediction.
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The results showed that the best validation scores were achieved by the CNN-LSTM model after integrating clinical attributes, with an accuracy of 92.47%.
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Our findings suggest that disease progression follows a stage-dependent MVD pattern that can identify at-risk regions.
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Meta-analyses of epidemiological studies have suggested that Alzheimer's disease (AD) may be linked with exposure to extremely low frequency (ELF) magnetic fields (MF).
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The four- and seven-class clustering solutions achieved high variance overlap with published microstate templates.
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49/100
Aggregated TDP-43 is a hallmark of frontotemporal dementia (FTD), amyotrophic lateral sclerosis (ALS), and limbic-predominant age-related TDP-43 encephalopathy (LATE), and a common co-pathology in other neurodegenerative diseases.
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In the within-language condition, combined speech timing and lexico-semantic features yielded maximal classification (area under the receiver operating characteristic curve [AUC]=0.88), outperforming single-feature models (AUC=0.79 for timing features; AUC=0.80 for lexico-semantic features).
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The aim of this study is to build and validate a model based on structural magnetic resonance imaging (sMRI) to predict the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD).
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ARIA-positive patients had significantly higher Fazekas scores compared with ARIA-negative patients (1.37 versus 1.0; P = .0262), indicating a greater DWMH burden.
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Among 1616 participants (AD = 1498, bvFTD = 118), all algorithms achieved high accuracy (80% to 90%) and discriminatory ability (AUC = 0.89 to 0.95).
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No adverse events occurred over the study period.
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Juxtacortical free water fraction, determined at diffusion MRI, was higher in participants with Alzheimer disease compared with cognitively normal controls and associated with amyloid-β, tau, and neurodegeneration biomarkers, revealing two distinct spatiotemporal progression patterns with different impacts on neuroanatomic signatures and cognitive performance.
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Alzheimer disease and its clinical variants have characteristic spatial and temporal progression patterns of amyloid and tau driving symptomatology, but the distribution of microglia density, as measured by 18kDa translocator protein (TSPO) PET, is unknown.
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Four progression pathways were concluded from our analyses, cognitive stability (32.8 %), sequential MCI-only decline (34.9 %), accelerated MCI-to-dementia progression (15.8 %), and rapid direct conversion (16.5 %).
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Physical inactivity and low fitness have become global health concerns.
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Fifty-three scans (31.74%) were rated amyloid-positive by all readers, while 62 scans (37.13%) were rated as amyloid-negative by all readers.
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Accurate differentiation of Alzheimer's disease (AD), frontotemporal dementia (FTD), and healthy control (HC) is critical for early diagnosis and intervention of brain disorders.
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Alzheimer disease (AD) is the most common dementing disorder, affecting 55 million people worldwide.
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Global CL quantification of Aβ PET scans is highly concordant with visual interpretation.
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Of 18 293 participants included in the parent study, scan images were available for 10 774 (59%), of which Centiloids were successfully calculated for 10 361 (96%).
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The SVM classifier achieved a notable accuracy of 96.01%, outperforming previously reported methods.
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50/100
Dementia, particularly Alzheimer's disease (AD), presents a growing global health challenge characterized by cognitive decline, behavioral changes, and loss of independence.
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A single-timepoint ResNet3D model achieved modest performance (c-index ~ 0.70).
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BackgroundAlzheimer's disease (AD) exhibits significant clinical and pathological heterogeneity, particularly during the mild cognitive impairment (MCI) transitional stage.
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31/100
Alzheimer's disease (AD) diagnosis using MRI scans must be very accurate since the subtle differences throughout the course of the disease are difficult to identify.
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The capability of xEEGNet to filter specific EEG bands, learns band specific topographies and use the right EEG spectral bands for disease classification demonstrates its interpretability.
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The advent of anti-amyloid therapies (AATs) for Alzheimer disease (AD) has elevated the importance of MRI surveillance for amyloid-related imaging abnormalities (ARIA) such as microhemorrhages and siderosis (ARIA-H) and edema (ARIA-E).
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The 3D Nested Hierarchical Transformer model helped to accurately diagnose Alzheimer disease based on structural brain MRI scans.
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< 0.001). Receiver operating characteristic (ROC) analysis identified right hippocampal MD and t-tau as optimal classifiers for AD.
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BackgroundAlzheimer's disease (AD) is a progressive neurodegenerative disorder frequently associated with dysphagia, leading to malnutrition, dehydration, and aspiration pneumonia.
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To the Editor: With the aging of the population, the prevalence of Alzheimer’s disease (AD) has increased significantly.
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This comprehensive review examines the integration of Quantitative Electroencephalography (qEEG) and Artificial Intelligence (AI) in the detection and diagnosis of Alzheimer's Disease (AD).
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The early and accurate diagnosis of Alzheimer's Disease and Frontotemporal Dementia remains a critical challenge, particularly with traditional machine learning models which often fail to provide transparency in their predictions, reducing user confidence and treatment effectiveness.
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Alzheimer disease (AD) is the leading cause of dementia, with an estimated 6.9 million Americans aged 65 and older living with Alzheimer dementia today, with this number projected to grow to 13.8 million by 2060.
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Utilizing machine learning, we identify a 12-protein panel from cerebrospinal fluid proteomic datasets.
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The integration of artificial intelligence in medical image classification has significantly advanced disease detection.
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47/100
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive function, posing a major global health challenge.
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46/100
Traditional diagnostic methods for Alzheimer's disease often suffer from low accuracy and lengthy processing times, delaying crucial interventions and patient care.
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This study developed machine learning models to predict Aβ positivity in Alzheimer’s disease by integrating early-phase 18 F-Florbetaben PET and clinical data to improve diagnostic accuracy.
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Accurate identification of neurodegenerative disorders, including Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD), is crucial for prompt medical intervention.
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The discovery cohort contained 3,671 individuals with a mean age of 68 years and consisted of 49% reported female.
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The proposed approach achieves 97.9% AD Classification Index (ADCI) for the Kaggle dataset and 97.9% ADCI for the MIRIAD dataset.
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Artificial intelligence (AI) is revolutionizing the field of medical imaging, offering unprecedented capabilities in data analysis, image interpretation, and decision support.
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The model achieves accuracy rates of 94% on the brain tumor dataset, 81% on the augmented Alzheimer dataset, and 93% on the third dataset, underscoring its capability to differentiate various neurological conditions.
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Dementia and mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) are neurological pathologies associated with disruptions in brain electromagnetic activity, typically studied using magnetoencephalography (MEG) and electroencephalography (EEG).
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Graph theory has proven to be useful in studying brain dysfunction in Alzheimer’s disease using MagnetoEncephaloGraphy (MEG) and fMRI signals.
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Three blood gene expression datasets of high dimensionality and low sample size (HDLSS) have been utilized in this study, with one dataset exhibiting significant class imbalance.
From across this tag
9 of the 838 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 9. The same 9 appear here every time.
- Volumetric MRI and cognitive measures in Alzheimer disease : comparison of markers of progression
- Measurement of longitudinal β-amyloid change with 18F-florbetapir PET and standardized uptake value ratios
- Gradual Disturbances of the Amplitude of Low-Frequency Fluctuations (ALFF) and Fractional ALFF in Alzheimer Spectrum
- Neuronal loss correlates with but exceeds neurofibrillary tangles in Alzheimer's disease
- Diagnostic accuracy of the MMSE in detecting probable and possible Alzheimer's disease in ethnically diverse highly educated individuals: an analysis of the NACC database
- Disruption of functional brain networks in Alzheimer's disease: what can we learn from graph spectral analysis of resting-state magnetoencephalography?
- A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer's dementia: China Aging and Neurodegenerative Initiative (CANDI) study
- Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Abeta42 in humans
- Robust automated detection of microstructural white matter degeneration in Alzheimer's disease using machine learning classification of multicenter DTI data