ArticleBrain : a journal of neurology2008
Accuracy of dementia diagnosis: a direct comparison between radiologists and a computerized method.
Article in Brain : a journal of neurology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers, 3 of them syntheses that pooled it.
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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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Who cites it
93 citing papers in PubMed, 3 syntheses or guidelines pooled it, 272 citations in OpenAlex.
- Interpretable machine learning for dementia: A systematic review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Pooled it
- Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review.Neuroradiology · 2021Pooled it
- Morphometric MRI as a diagnostic biomarker of frontotemporal dementia: A systematic review to determine clinical applicability.NeuroImage. Clinical · 2018Pooled it
- Antemortem differential diagnosis of dementia pathology using structural MRI: Differential-STAND.NeuroImage · 2011Trial
- Beyond complex architectures: a streamlined CNN pipeline for robust Alzheimer's disease classification from brain MRI.Neuroradiology · 2026Article
- X-ViTCNN: A Novel Network-Level Fusion of Transfer Learning and Customized Vision Transformer for Multi-Stage Alzheimer's Disease Prediction Using MRI Scans.Diagnostics (Basel, Switzerland) · 2026Article
- Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias.Seminars in neurology · 2026Review
- A computational ontology framework for the synthesis of multi-level pathology reports from brain MRI scans.Journal of Alzheimer's disease : JAD · 2025Article
- Machine learning diagnosis of cognitive impairment and dementia in harmonized older adult cohorts.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Application of artificial intelligence in Alzheimer's disease: a bibliometric analysis.Frontiers in neuroscience · 2025Review
- Augmenting radiological assessment of imaging evident dementias with radiomic analysis.NPJ dementia · 2025Article
- Machine Learning for Mental Health: Applications, Challenges, and the Clinician's Role.Current psychiatry reports · 2024Review
- Voxel-based morphometry in single subjects without a scanner-specific normal database using a convolutional neural network.European radiology · 2024Article
- Inclusion of a computerized test in ADHD diagnosis process can improve trust in the specialists' decision and elevate adherence levels.Scientific reports · 2024Observational
- Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA.Scientific reports · 2023Article
- New cases of dementia are rising in elderly populations in Wales, UK.Journal of the neurological sciences · 2023Article
- Article
- Automated Medical Diagnosis of Alzheimer´s Disease Using an Efficient Net Convolutional Neural Network.Journal of medical systems · 2023Article
- Higher performance for women than men in MRI-based Alzheimer's disease detection.Alzheimer's research & therapy · 2023Article
- Deep Learning Techniques for the Effective Prediction of Alzheimer's Disease: A Comprehensive Review.Healthcare (Basel, Switzerland) · 2022Review
33 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
14 authors at 7 institutions in 5 countries.
Funding
Abstract
There has been recent interest in the application of machine learning techniques to neuroimaging-based diagnosis. These methods promise fully automated, standard PC-based clinical decisions, unbiased by variable radiological expertise. We recently used support vector machines (SVMs) to separate sporadic Alzheimer's disease from normal ageing and from fronto-temporal lobar degeneration (FTLD). In this study, we compare the results to those obtained by radiologists. A binary diagnostic classification was made by six radiologists with different levels of experience on the same scans and information that had been previously analysed with SVM. SVMs correctly classified 95% (sensitivity/specificity: 95/95) of sporadic Alzheimer's disease and controls into their respective groups. Radiologists correctly classified 65-95% (median 89%; sensitivity/specificity: 88/90) of scans. SVM correctly classified another set of sporadic Alzheimer's disease in 93% (sensitivity/specificity: 100/86) of cases, whereas radiologists ranged between 80% and 90% (median 83%; sensitivity/specificity: 80/85). SVMs were better at separating patients with sporadic Alzheimer's disease from those with FTLD (SVM 89%; sensitivity/specificity: 83/95; compared to radiological range from 63% to 83%; median 71%; sensitivity/specificity: 64/76). Radiologists were always accurate when they reported a high degree of diagnostic confidence. The results show that well-trained neuroradiologists classify typical Alzheimer's disease-associated scans comparable to SVMs. However, SVMs require no expert knowledge and trained SVMs can readily be exchanged between centres for use in diagnostic classification. These results are encouraging and indicate a role for computerized diagnostic methods in clinical practice.
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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.