ArticleScientific reports2018
Machine learning identified an Alzheimer's disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson's disease dementia.
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 38 papers, 3 of them syntheses 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
38 citing papers in PubMed, 3 syntheses or guidelines pooled it, 71 citations in OpenAlex.
- Pooled it
- Diagnostic performance of molecular imaging methods in predicting the progression from mild cognitive impairment to dementia: an updated systematic review.European journal of nuclear medicine and molecular imaging · 2024Pooled it
- Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions.Journal of medical systems · 2023Pooled it
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.Biosensors · 2026Review
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- Meta analysis of glucose metabolism across Alzheimer's, Parkinson's and ALS Reveals emergence of adaptive brain glucometabolic responses and associated neurological functional profiles.medRxiv : the preprint server for health sciences · 2026Article
- Metabolic brain networks in dementia with Lewy bodies: from prodromal to manifest disease stages.Journal of neurology, neurosurgery, and psychiatry · 2026Article
- Bidirectional prognostic predictions of conversion and reversion in amnestic mild cognitive impairment.GeroScience · 2026Article
- Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer's disease.Scientific reports · 2025Article
- Review
- Radionuclide Imaging of the Neuroanatomical and Neurochemical Substrate of Cognitive Decline in Parkinson's Disease.Nuclear medicine and molecular imaging · 2024Review
- Investigating neural markers of Alzheimer's disease in posttraumatic stress disorder using machine learning algorithms and magnetic resonance imaging.Frontiers in neurology · 2024Article
- Secure medical image transmission using deep neural network in e-health applications.Healthcare technology letters · 2023Article
- Hypermetabolic Cerebellar Connectome in Alzheimer's Disease.Brain connectivity · 2023Article
- Do Epilepsy Patients with Cognitive Impairment Have Alzheimer's Disease-like Brain Metabolism?Biomedicines · 2023Article
- Brain Hemisphere Dissimilarity, a Self-Supervised Learning Approach for alpha-synucleinopathies prediction with FDG PET.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- Functional brain networks in the evaluation of patients with neurodegenerative disorders.Nature reviews. Neurology · 2023Review
- Parkinson's disease progression: Increasing expression of an invariant common core subnetwork.NeuroImage. Clinical · 2023Article
- Predicting cognitive decline in Parkinson's disease using FDG-PET-based supervised learning.The Journal of clinical investigation · 2022Article
- Identification and validation of Alzheimer's disease-related metabolic brain pattern in biomarker confirmed Alzheimer's dementia patients.Scientific reports · 2022Article
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 at 4 institutions in 2 countries.
Funding
Abstract
Utilizing the publicly available neuroimaging database enabled by Alzheimer's disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/ ), we have compared the performance of automated classification algorithms that differentiate AD vs. normal subjects using Positron Emission Tomography (PET) with fluorodeoxyglucose (FDG). General linear model, scaled subprofile modeling and support vector machines were examined. Among the tested classification methods, support vector machine with Iterative Single Data Algorithm produced the best performance, i.e., sensitivity (0.84) × specificity (0.95), by 10-fold cross-validation. We have applied the same classification algorithm to four different datasets from ADNI, Health Science Centre (Winnipeg, Canada), Dong-A University Hospital (Busan, S. Korea) and Asan Medical Centre (Seoul, S. Korea). Our data analyses confirmed that the support vector machine with Iterative Single Data Algorithm showed the best performance in prediction of future development of AD from the prodromal stage (mild cognitive impairment), and that it was also sensitive to other types of dementia such as Parkinson's Disease Dementia and Dementia with Lewy Bodies, and that perfusion imaging using single photon emission computed tomography may achieve a similar accuracy to that of FDG-PET.
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.