ArticleJournal of Alzheimer's disease : JAD2015
Applying Automated MR-Based Diagnostic Methods to the Memory Clinic: A Prospective Study.
Article in Journal of Alzheimer's disease : JAD, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 6 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- 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
- Artificial intelligence in the care of children and adolescents with chronic diseases: a systematic review.European journal of pediatrics · 2024Pooled it
- Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review.Neuroradiology · 2021Pooled it
- Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment.The Cochrane database of systematic reviews · 2020Pooled it
- Morphometric MRI as a diagnostic biomarker of frontotemporal dementia: A systematic review to determine clinical applicability.NeuroImage. Clinical · 2018Pooled it
- Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review.PloS one · 2017Pooled it
- Alzheimer's disease diagnosis support for brain perfusion SPECT scans in a real-world clinical cohort.Journal of Alzheimer's disease : JAD · 2026Article
- The potential role of machine learning and deep learning in differential diagnosis of Alzheimer's disease and FTD using imaging biomarkers: A review.The neuroradiology journal · 2025Review
- Real-world brain imaging in a population-based cohort enables accurate markers for dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Removing outliers from the normative database improves regional atrophy detection in single-subject voxel-based morphometry.Neuroradiology · 2024Article
- Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA.Scientific reports · 2023Article
- The use of neuroimaging techniques in the early and differential diagnosis of dementia.Molecular psychiatry · 2023Review
- Identifying healthy individuals with Alzheimer's disease neuroimaging phenotypes in the UK Biobank.Communications medicine · 2023Article
- Adaptive data-driven selection of sequences of biological and cognitive markers in pre-clinical diagnosis of dementia.Scientific reports · 2023Article
- Article
- White matter microstructure alterations in frontotemporal dementia: Phenotype-associated signatures and single-subject interpretation.Brain and behavior · 2022Article
- Impact of normative brain volume reports on the diagnosis of neurodegenerative dementia disorders in neuroradiology: A real-world, clinical practice study.Frontiers in aging neuroscience · 2022Article
- Effect of MRI acquisition acceleration via compressed sensing and parallel imaging on brain volumetry.Magma (New York, N.Y.) · 2021Article
- An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer's disease.Alzheimer's research & therapy · 2021Article
Corrections and comments
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Authors and funding
19 authors.
Funding
Abstract
Several studies have demonstrated that fully automated pattern recognition methods applied to structural magnetic resonance imaging (MRI) aid in the diagnosis of dementia, but these conclusions are based on highly preselected samples that significantly differ from that seen in a dementia clinic. At a single dementia clinic, we evaluated the ability of a linear support vector machine trained with completely unrelated data to differentiate between Alzheimer's disease (AD), frontotemporal dementia (FTD), Lewy body dementia, and healthy aging based on 3D-T1 weighted MRI data sets. Furthermore, we predicted progression to AD in subjects with mild cognitive impairment (MCI) at baseline and automatically quantified white matter hyperintensities from FLAIR-images. Separating additionally recruited healthy elderly from those with dementia was accurate with an area under the curve (AUC) of 0.97 (according to Fig. 4). Multi-class separation of patients with either AD or FTD from other included groups was good on the training set (AUC > 0.9) but substantially less accurate (AUC = 0.76 for AD, AUC = 0.78 for FTD) on 134 cases from the local clinic. Longitudinal data from 28 cases with MCI at baseline and appropriate follow-up data were available. The computer tool discriminated progressive from stable MCI with AUC = 0.73, compared to AUC = 0.80 for the training set. A relatively low accuracy by clinicians (AUC = 0.81) illustrates the difficulties of predicting conversion in this heterogeneous cohort. This first application of a MRI-based pattern recognition method to a routine sample demonstrates feasibility, but also illustrates that automated multi-class differential diagnoses have to be the focus of future methodological developments and application studies.
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