ArticleRadiology2023
MRI-based Deep Learning Assessment of Amyloid, Tau, and Neurodegeneration Biomarker Status across the Alzheimer Disease Spectrum.
Article in Radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 25 citations in OpenAlex.
- Classification of tau status with machine learning models in amyloid-positive cohorts.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- MRI In Vivo Detection of Amyloid-β Protein Deposition in Different Brain Regions of Patients with AD and MCI.Brain topography · 2026Article
- Sim-to-Real Domain Adaptation for Early Alzheimer's Detection from Handwriting Kinematics Using Hybrid Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- Common and rare variant analyses implicateNPJ dementia · 2026Article
- Review of deep learning models for Alzheimer's disease detection: MRI-centric approaches and multimodal extensions.Frontiers in artificial intelligence · 2026Review
- Neuropathologically validated MRI to tau PET synthesis via Covariate-modulated attention networks.bioRxiv : the preprint server for biology · 2025Article
- Multimodal Self-Supervised Learning for Early Alzheimer's: Cross-Modal MRI-PET, Longitudinal Signals, and Site Invariance.Diagnostics (Basel, Switzerland) · 2025Article
- Deep Learning-Based Prediction of PET Amyloid Status Using MRI.AJNR. American journal of neuroradiology · 2025Article
- Generating MRI-Derived CSF Proxy-Markers to Predict and Visualize Alzheimer's Disease Progression.Human brain mapping · 2025Article
- Automatic design and optimization of MRI-based neurochemical sensors via reinforcement learning.Discover nano · 2025Article
- Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction.Alzheimer's research & therapy · 2025Article
- Deep learning-based Alzheimer's disease detection using magnetic resonance imaging and gene expression data.PloS one · 2025Article
- From pixels to prognosis: radiomics and AI in Alzheimer's disease management.Frontiers in neurology · 2025Review
- Genome-wide scan of Flortaucipir PET levels findsmedRxiv : the preprint server for health sciences · 2024Article
- Understanding machine learning applications in dementia research and clinical practice: a review for biomedical scientists and clinicians.Alzheimer's research & therapy · 2024Review
- Early Diagnosis of Neurodegenerative Diseases: What Has Been Undertaken to Promote the Transition from PET to Fluorescence Tracers.Molecules (Basel, Switzerland) · 2024Review
- Multi-modal deep learning and explainable AI for predicting multiple dementia-related neuropathologies from brain MRI, clinical, and genetic data.Frontiers in neurologyArticle
Corrections and comments
- Erratum issued
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Background PET can be used for amyloid-tau-neurodegeneration (ATN) classification in Alzheimer disease, but incurs considerable cost and exposure to ionizing radiation. MRI currently has limited use in characterizing ATN status. Deep learning techniques can detect complex patterns in MRI data and have potential for noninvasive characterization of ATN status. Purpose To use deep learning to predict PET-determined ATN biomarker status using MRI and readily available diagnostic data. Materials and Methods MRI and PET data were retrospectively collected from the Alzheimer's Disease Imaging Initiative. PET scans were paired with MRI scans acquired within 30 days, from August 2005 to September 2020. Pairs were randomly split into subsets as follows: 70% for training, 10% for validation, and 20% for final testing. A bimodal Gaussian mixture model was used to threshold PET scans into positive and negative labels. MRI data were fed into a convolutional neural network to generate imaging features. These features were combined in a logistic regression model with patient demographics,
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.