ArticleScientific reports2018
Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers, 5 of them syntheses that pooled it.
What it found
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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.
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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.
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Who cites it
130 citing papers in PubMed, 5 syntheses or guidelines pooled it, 375 citations in OpenAlex.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review.Alzheimer's research & therapy · 2021Pooled it
- A Systematic Review and Implementation of IoT-Based Pervasive Sensor-Enabled Tracking System for Dementia Patients.Journal of medical systems · 2019Pooled it
- Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data.Frontiers in aging neuroscience · 2019Pooled it
- A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease.Scientific reports · 2021Trial
- Topological Data Analysis-Driven fNIRS Signal Processing for Alzheimer's Disease Stage Identification.Sensors (Basel, Switzerland) · 2026Article
- Comparative Analysis of Deep Learning Techniques in Alzheimer's Disease Diagnosis: Trends, Challenges, and Future Directions.MethodsX · 2026Review
- Automated diagnosis of pulmonary nodules in 3D PET/CT images using dual-path densely connected networks with cross-modal fusion.Quantitative imaging in medicine and surgery · 2026Article
- Review of deep learning models for Alzheimer's disease detection: MRI-centric approaches and multimodal extensions.Frontiers in artificial intelligence · 2026Review
- Impact of image preprocessing methods on MRI radiomics feature variability and classification performance in Parkinson's disease motor subtype analysis.Scientific reports · 2025Article
- Characteristics of brain glucose metabolism in Parkinson's disease patients with freezing of gait: a study based onBMC neurology · 2025Article
- FDG-PET Image Classification in Alzheimer's Disease: from Traditional Visual Analysis to Advanced Transfer Learning.Nuclear medicine and molecular imaging · 2025Article
- Article
- Quantitative magnetic resonance imaging in Alzheimer's disease: a narrative review.Quantitative imaging in medicine and surgery · 2025Review
- Machine-learning models for Alzheimer's disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation.Brain informatics · 2025Review
- Reducing inference cost of Alzheimer's disease identification using an uncertainty-aware ensemble of uni-modal and multi-modal learners.Scientific reports · 2025Article
- Integration of multimodal imaging data with machine learning for improved diagnosis and prognosis in neuroimaging.Frontiers in human neuroscience · 2025Article
- Comprehensive evaluation and clinical implications of kernel extreme learning machine long short term memory transformer framework.American journal of translational research · 2025Article
- AD-Diff: enhancing Alzheimer's disease prediction accuracy through multimodal fusion.Frontiers in computational neuroscience · 2025Article
70 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 20 institutions in 2 countries.
Funding
Abstract
Alzheimer's Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1-3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature.
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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 OpenQuestion graph.