ArticleRevista brasileira de psiquiatria (Sao Paulo, Brazil : 1999)
Support vector machine-based classification of neuroimages in Alzheimer's disease: direct comparison of FDG-PET, rCBF-SPECT and MRI data acquired from the same individuals.
Article in Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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
18 citing papers in PubMed, 1 synthesis or guideline 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
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- A mini review of transforming dementia care in China with data-driven insights: overcoming diagnostic and time-delayed barriers.Frontiers in aging neuroscience · 2025Review
- A Fuzzy Cognitive Map-based Framework for Alzheimer's Disease Diagnosis Using Multimodal Magnetic Resonance Imaging-Positron Emission Tomography Registration.Journal of medical signals and sensors · 2025Article
- Explainable AI-based Deep-SHAP for mapping the multivariate relationships between regional neuroimaging biomarkers and cognition.European journal of radiology · 2024Article
- Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data.Scientific reports · 2023Article
- Photon-counting statistics-based support vector machine with multi-mode photon illumination for quantum imaging.Scientific reports · 2022Article
- Development and evaluation of a T1 standard brain template for Alzheimer disease.Quantitative imaging in medicine and surgery · 2021Article
- Artificial intelligence for molecular neuroimaging.Annals of translational medicine · 2021Review
- Neuroimaging Research on Dementia in Brazil in the Last Decade: Scientometric Analysis, Challenges, and Peculiarities.Frontiers in neurology · 2021Review
- Brain SPECT as a Biomarker of Neurodegeneration in Dementia in the Era of Molecular Imaging: Still a Valid Option?Frontiers in neurology · 2021Review
- Denouements of machine learning and multimodal diagnostic classification of Alzheimer's disease.Visual computing for industry, biomedicine, and art · 2020Review
- Classifying intracranial stenosis disease severity from functional MRI data using machine learning.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2020Article
- The risk prediction of Alzheimer's disease based on the deep learning model of brain 18F-FDG positron emission tomography.Saudi journal of biological sciences · 2020Article
- CCCDTD5: Clinical role of neuroimaging and liquid biomarkers in patients with cognitive impairment.Alzheimer's & dementia (New York, N. Y.) · 2020Review
- Psychiatric neuroimaging research in Brazil: historical overview, current challenges, and future opportunities.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2020Article
- Neurotheranostics as personalized medicines.Advanced drug delivery reviews · 2019Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo conduct the first support vector machine (SVM)-based study comparing the diagnostic accuracy of T1-weighted magnetic resonance imaging (T1-MRI), F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and regional cerebral blood flow single-photon emission computed tomography (rCBF-SPECT) in Alzheimer's disease (AD).
methodBrain T1-MRI, FDG-PET and rCBF-SPECT scans were acquired from a sample of mild AD patients (n=20) and healthy elderly controls (n=18). SVM-based diagnostic accuracy indices were calculated using whole-brain information and leave-one-out cross-validation.
resultsThe accuracy obtained using PET and SPECT data were similar. PET accuracy was 68∼71% and area under curve (AUC) 0.77∼0.81; SPECT accuracy was 68∼74% and AUC 0.75∼0.79, and both had better performance than analysis with T1-MRI data (accuracy of 58%, AUC 0.67). The addition of PET or SPECT to MRI produced higher accuracy indices (68∼74%; AUC: 0.74∼0.82) than T1-MRI alone, but these were not clearly superior to the isolated neurofunctional modalities.
conclusionIn line with previous evidence, FDG-PET and rCBF-SPECT more accurately identified patients with AD than T1-MRI, and the addition of either PET or SPECT to T1-MRI data yielded increased accuracy. The comparable SPECT and PET performances, directly demonstrated for the first time in the present study, support the view that rCBF-SPECT still has a role to play in AD diagnosis.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.