Article

Generalizable Prediction of Alzheimer Disease Pathologies with a Scalable Annotation Tool and an High-Accuracy Model

2025-02-05

Abstract excerpt

Characterizing the cardinal neuropathologies in Alzheimer disease (AD) can be laborious, time consuming, and susceptible to intra- and inter-observer variability. The lack of high throughput unbiased approaches to reliably assess neuropathology hampers efforts to use pathology as a means to link clinical features of AD to molecular pathogenesis in the ever-growing datasets of persons with AD. To remove this roadbl...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 2942bfa1-f399-5e05-a44d-3b4461c25cf7
DOI: 10.1101/2025.02.03.25321603

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Generalizable Prediction of Alzheimer Disease Pathologies with a Scalable Annotation Tool and an High-Accuracy ModelDOI 10.1101/2025.02.03.25321603Read

A Scalable High Throughput Fully Automated Pipeline for the Quantification of Amyloid Pathology in Alzheimer’s Disease using Deep Learning AlgorithmsSemantic proximity Read

Advances in Deep Neuropathological Phenotyping of Alzheimer Disease: Past, Present, and Future.Semantic proximity Read

Machine Learning Classification of Alzheimer’s Disease Pathology Reveals Diffuse Amyloid as a Major Predictor of Cognitive Impairment in Human Hippocampal SubregionsSemantic proximity Read

Mapping Alzheimer′s neuropathology signatures to the whole brain transcriptome using machine learning data fusionSemantic proximity Read

Learning precise segmentation of neurofibrillary tangles from rapid manual point annotationsSemantic proximity Read

A unified model for staging amyloid and tau pathology in Alzheimer’s diseaseSemantic proximity Read

Deep Learning for Alzheimer’s Disease: Mapping Large-scale Histological Tau Protein for Neuroimaging Biomarker ValidationSemantic proximity Read

Predicting Autopsy-Confirmed Neuropathology across Clinical, Neuroimaging, and CSF Biomarkers using Machine LearningSemantic proximity Read

Automated segmentation by deep learning of neuritic plaques and neurofibrillary tangles in brain sections of Alzheimer’s Disease PatientsSemantic proximity Read

Evaluation of Deep Learning Algorithms to Predict Multiple Dementia-Related Neuropathologies from Brain MRI, Clinical and Genetic DataSemantic proximity Read

A large-scale multi-centre study characterising atrophy heterogeneity in Alzheimer’s diseaseSemantic proximity Read

A Pathway-Based Machine Learning Approach Identifies Region-Specific Markers and Patterns in Alzheimer’s Disease Patients Based on Spatial and Severity MetadataSemantic proximity Read